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************************************************************************* Evolutionary
systems, Evolvable Machines, artificial
evolution, ACM SIGEVOlution bioinspired
systems swarm intelligence ant
colony algorithms ************************************************************************* Evolutionary Systems Design
/ Diseño de sistemas evolutivos The General
Evolution Research Group “The General Evolution
Research Group (GERG) began with a secret meeting in Budapest in 1984 of
scientists from both sides of the Iron Curtain during a critical juncture in
the Cold War. Spurred by the mounting threat to our species of rapid nuclear
proliferation and overkill, the purpose was to see if it might be possible to
use the chaos theory then coming into vogue to develop a new general theory
of evolution that might serve as a road map for our species out of the
mounting chaos of our times to the reassuring order of a better world. Out of this
beginning, founded by general evolution theorist Ervin Laszlo and a handful
of original co-founders, over the years GERG has expanded into a small
informal research group of 40 scientists in most of the major fields of social
as well as natural science.” (FGS Link,
28/vii/2010) http://www.thedarwinproject.com/gerg/gerg.html Evolutionary
systems design: Policy making under complexity and group decision support
systems Holden-Day
series in explorations in operations research, systems, and artificial
intelligence, Oakland, Calif. 1988 (FGS Link,
28/vii/2010) http://simulabor.com/?PUB=102646521&showStat=Ratings Diseño de sistemas evolutivos Melvin F.
Shakun, New York University, 1988 Publisher:
Springer Berlin / Heidelberg (FGS Link,
28/vii/2010) http://www.springerlink.com/content/x837842937064124/ The Evolution
of Evolutionary Systems Design Authors:
Laszlo
A.; Laszlo K. Source: World Futures: The
Journal of General Evolution, Volume 58, Numbers 5-6, 1 January 2002 ,
pp. 351-363(13) Publisher:
Routledge, part of the Taylor & Francis Group The Graduate School
of Business Administration and Leadership (EGADE) of the Monterrey Institute
of Technology (ITESM) Abstract: “This article
presents the genesis of Evolutionary Systems Design (ESD) as a praxis that
draws on General Evolution Theory and Social Systems Design methodology, in
addition to Critical Systems Theory, to engage in lifelong learning and human
development in partnership with the Earth. The contributions of Bela H.
Banathy to the creation of ESD are portrayed as bridging evolutionary
consciousness and evolutionary action. Following a brief description of the
inspiration and mentorship provided by Bela in this regard, the roots of ESD
are traced back to General Evolution Theory. It is described how notions of
evolutionary stewardship grew out of encounters with Bela and his work at the
International Systems Institute, and were given operational viability through
the methodology of Social Systems Design he developed. The fundamental tenets
of ESD are presented and discussed by way of a four-stage evolutionary
learning framework. Finally, the vehicle of Evolutionary Learning Community
through which ESD operates is shown to embody the potential for individuals
and groups to think, live, and act in harmony with the dynamics of which they
are a part as a means to guide the conscious creation of sustainability.” (FGS Link,
28/vii/2010) http://www.ingentaconnect.com/content/routledg/gwof/2002/00000058/F0020005/art00002 Evolutionary
Systems Design Resources (FGS Link,
28/vii/2010) http://archive.syntonyquest.org/elcTree/resources.html#sysdesign Béla H. Bánáthy “Béla
Heinrich Bánáthy (December 1, 1919 – September 4, 2003) was a Hungarian linguist, systems scientist and a professor at San José State University and UC Berkeley. Bánáthy was the
founder of the White Stag Leadership
Development Program whose leadership model was adopted across the United
States. He is also founder of the International Systems Institute[1] with its
innovative "conversation"-oriented conference structure, co-founder
of the General Evolutionary Research Group[2], an
influential professor of systems theory and a widely-read and respected
author.” (Wikipedia,
28/vii/2010) http://en.wikipedia.org/wiki/B%C3%A9la_H._B%C3%A1n%C3%A1thy What is Evolutionary Learning Community? “Evolutionary
Learning Communities (ELCs) are flexible environments where people can learn about
the interconnected nature of our world, the ecological impact of our individual and
collective choices, and the joy of finding a meaningful way to contribute to our
communities. They are learning spaces that embrace and reflect values of partnership and
sustainability for a higher quality of life.” (FGS Link,
28/vii/2010) http://archive.syntonyquest.org/elcTree/resourcesPDFs/ELC.pdf ************************************************************************* Evolution
of Cooperation The Evolution
of Cooperation Robert Axelrod Professor of Political
Science and Public Policy, University of Michigan, Ann Arbor. Dr. Axelrod is
a member of the American National Academy of Sciences and the American
Academy of Arts and Sciences. His honors include a MacArthur Foundation
Fellowship for the period 1987 through 1992. “Under what
conditions will cooperation emerge in a world of egoists without central
authority? This question has intrigued people for a long time. We all know
that people are not angels, and that they tend to look after themselves and their
own first. Yet we also know that cooperation does occur and that our
civilization is based upon it. A good example
of the fundamental problem of cooperation is the case where two industrial
nations have erected trade barriers to each other’s exports. Because of the
mutual advantages of free trade, both countries would be better off if these
barriers were eliminated. But if either country were to eliminate its
barriers unilaterally, it would find itself facing terms of trade that hurt
its own economy. In fact, whatever one country does, the other country is
better off retaining its own trade barriers. Therefore, the problem is that
each country has an incentive to retain trade barriers, leading to a worse
outcome than would have been possible had both countries cooperated with each
other…” (FGS Link,
28/vii/2010) http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.117.7463&rep=rep1&type=pdf Book The Evolution
of Cooperation. Robert Axelrod,
New York, Basic Books, 1984. (FGS Link,
28/vii/2010) http://www-personal.umich.edu/~axe/Axelrod_Evol_of_Coop_excerpts.pdf The Evolution
of Cooperation “This article
is an introduction to how game theory and computer modeling are illuminating
certain aspects of moral and political philosophy, particularly the role
of individuals in groups, the "biology of selfishness and altruism"[2],
and how cooperation
can be evolutionarily advantageous.” (Wikipedia, 28/vii/2010) http://en.wikipedia.org/wiki/The_Evolution_of_Cooperation ************************************************************************* Evolution equation / Ecuaciones de la Evolucion Evolution
equation “An equation
that can be interpreted as the differential law of the development
(evolution) in time of a system. The term does not have an exact definition, and
its meaning depends not only on the equation itself, but also on the
formulation of the problem for which it is used. Typical of an evolution
equation is the possibility of constructing the solution from a prescribed
initial condition that can be interpreted as a description of the initial
state of the system...” (FGS Link,
28/vii/2010) http://eom.springer.de/e/e036690.htm Dynamical
Systems and Evolution Equations: Theory and Applications Mathematical
concepts and methods in science and engineering Vol. 20 New York :
Plenum Press, c1980. John Andrew
Walker (FGS Link,
28/vii/2010) Dynamical
Systems and Evolution Equations: Theory and Applications. By J. A.
Walker. Plenum Publishing Corp. New York. 1980. Pages
viii-236. Price $29.50. REVIEWED by T.
K. CAUGHEY Transactions of
the ASME, VOL. 48, JUNE 198, 450 “This is the
first book to give a systematic account of the considerable progress which
has been made in extending the geometrical methods, developed for ordinary
differential equations, to systems whose evolutionary equations are partial,
delay or functional differential equations. The difficulty with such systems
is that their state spaces are infinite dimensional and not ideally compact,
unlike ordinary differential equations where state spaces are finite
dimensional. The book is
organized into five chapters. The first chapter discusses the general ideas
and illustrates the techniques for finite dimensional systems. Chapters II,
III, and IV extend the introductory treatmentof Chapter I to a general metric
space framework. Chapter II summarizes much of the mathematics needed for
this extension, Chapter III discusses abstract evolution equations in Banach
spaces and Chapter IV describes some of the more useful ideas of topological
dynamics. The extension of Liapunov's direct method for investigating
stability and asymptotic behavior by the invariance principle is studied in
some detail. Attention is focused throughout the book on dynamical systems
(the autonomous case) while processes (the nonautonomous case) are mentioned
only briefly. Chapter V contains some recent applications of the theory to
physical systems ranging from supersonic panel flutter to the stability of a
nuclear reactor. (FGS Link,
28/vii/2010) http://scitation.aip.org/getpdf/servlet/GetPDFServlet?filetype=pdf&id=JAMCAV000048000002000450000001&idtype=cvips&prog=normal&bypassSSO=1 Integrable
Evolution Equations on Associative Algebras. by Peter J.
Olver , Vladimir V. Sokolov Comm. Math.
Phys, March 5, 1997 Abstract: “This paper surveys
the classification of integrable evolution equations whose field variables
take values in an associative algebra, which includes matrix, Clifford, and
group algebra valued systems. A variety of new examples of integrable systems
possessing higher order symmetries are presented. Symmetry reductions lead to
associative algebra-valued version of the Painlev'e transcendent equations.
The basic theory of Hamiltonian structures for associative algebra-valued
systems is developed and the biHamiltonian structures for several examples
are found.” (FGS Link,
28/vii/2010) http://www.ima.umn.edu/preprints/scanned-preprint/preprints5/1473.pdf Two-Field Integrable Evolutionary Systems of the Third Order and
Their Differential Substitutions Anatoly G. MESHKOV and Maxim Ju. BALAKHNEV Orel State Technical University, Orel, Russia Received October 04, 2007, in final form January 17, 2008; Published
online February 09, 2008 Original article is available at http://www.emis.de/journals/SIGMA/2008/018/ Symmetry, Integrability
and Geometry: Methods and Applications SIGMA 4 (2008), 018, 29 pages Abstract. A list of forty third-order exactly integrable two-field
evolutionary systems ispresented. Differential substitutions connecting
various systems from the list are found. Itis proved that all the systems can
be obtained from only two of them. Examples of zerocurvature representations
with 4 × 4 matrices are presented. (FGS Link,
28/vii/2010) http://www.emis.de/journals/SIGMA/2008/018/sigma08-018.pdf ************************************************************************* Outline for a
Logical Theory of Adaptive Systems University of Michigan, Ann Arbor, Michigan, July 1962 Journal of the ACM (JACM) , Volume 9 Issue 3, July 1962 Publisher: ACM Introduction “The purpose of this paper is to
outline a theory of automata appropriate to the properties, requirements and
questions of adaptation. The conditions that such a theory should satisfy
come from not one but several fields: It should be possible to formulate, at
least in an abstract version, some of the key hypotheses and problems from
relevant parts of biology, particularly the areas concerned with molecular
control and neurophysiology. The work in theoretical genetics initiated by R.
A. Fisher [5] and Sewall Wright [24] should find a natural place in the
theory. At the same time the rigorous methods of automata theory should be
brought to bear (particularly those parts concerned with growing automata [1,
2, 3, 7, 8, 12, 15, 18, 23]). Finally the theory should include among its
models abstract counterparts of artificial adaptive systems currently being
studied, systems such as Newell-Shaw-Simon's "General Problem
Solver" [13], Selfridge's "Pandemonium" [17], von Neumann's self-reproducing automata
[22] and Turing's morphogenetic systems [19, 20]...” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=321127.321128&coll=ACM&dl=ACM&CFID=46991025&CFTOKEN=30081638 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=321128&type=pdf&coll=ACM&dl=ACM&CFID=46991025&CFTOKEN=30081638 ************************************** Induction:
processes of inference, learning, and discovery John H.
Holland, Keith J.
Holyoak, Richard E.
Nisbett. Paul R.
Thagard Univ. of Michigan, Ann Arbor, November 1986 Book Publisher MIT Press Cambridge,
MA, USA, Pages: 385, 1986 (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=6677&coll=ACM&dl=ACM&CFID=46991025&CFTOKEN=30081638 Induction: processes of inference,
learning, and discovery MIT Press Cambridge, MA, USA Product
Description “Two psychologists, a computer scientist, and a
philosopher have collaborated to present a framework for understanding
processes of inductive reasoning and learning in organisms and machines.
Theirs is the first major effort to bring the ideas of several disciplines to
bear on a subject that has been a topic of investigation since the time of
Socrates. The result is an integrated account that treats problem solving and
induction in terms of rulebased mental models. John Holland is Professor of Electrical Engineering
at Michigan University. Keith Holyoak is Professor of Psychology at the
University of California, Los Angeles. Richard Nisbett is Professor of
Psychology at the University of Michigan and Paul Thagard is Research
Scientist at Princeton University's Cognitive Science Laboratory Induction
is included in the Computational Models of Cognition and Perception Series. A
Bradford Book.”
http://www.amazon.com/exec/obidos/ASIN/0262081601/acmorg-20 ************************************** Genetic
algorithms and classifier systems: foundations and future directions John H.
Holland, October 1987 Proceedings of the Second International Conference
on Genetic Algorithms on Genetic algorithms and their application, Pages: 82 - 89 Cambridge, Massachusetts, United
States,1987 Publisher L. Erlbaum Associates Inc. Hillsdale, NJ, USA (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=42512.42524&coll=ACM&dl=ACM&CFID=46991025&CFTOKEN=30081638 ************************************************************************* Computer
Science Department, Rutgers University, New Brunswick NJ, January 1983 POPL '83: Proceedings of the 10th ACM SIGACT-SIGPLAN symposium on Principles of
programming languages Publisher: ACM ABSTRACT “This
paper proposes a technique for what we call localization of power in computer
systems, which can be viewed as a generalization of such linguistic
disciplines as scope rules, strong typing and data-abstraction.
Although the proposed technique is conceptually based on the theory of
protection, it is presented as a rather simple extension of the package
construct of the Ada language. This technique is expected to be beneficial
for software engineering in several ways. In particular It
facilitates reasoning about large scale systems, by allowing one to ignore
most of the details of the system when reasoning about specific aspects of
it. It
provides us with a generalization of the conventional concept of
data-abstraction, by allowing the formation of several different abstractions
for the same type of objects, and by supporting “interactions” between the
abstractions of different types. It allows
us to provide parts of a system with a certain ability to control the
activity of the rest of it. It
supports a broad spectrum of policies for the design and management of large scale,
evolving systems” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=567067.567095&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=567095&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 ************************************** System = program + users + law Rutgers
Univ., New Brunswick, NJ, December 1987 ICAIL '87: Proceedings of the 1st international conference on Artificial
intelligence and law Publisher: ACM ABSTRACT “This
paper is based on a new approach for dealing with large scale software
systems. This approach is based on the concept of a Law-Governed System,
which is a triple >program, users, law< where the law is an explicit
and strictly enforced set of rules about the structure and operation of the
program, and about the evolution of the entire system. We describe Darwin, a
skeleton of a programming environment that supports our notion of
law-governed systems. Darwin is based on the object-oriented programming
paradigm and uses logic programming to express and enforce the law.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=41735.41755&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=41755&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 ************************************** A software
development environment for law-governed systems Naftaly H.
Minsky, David
Rozenshtein Rutgers Univ., New Brunswick, NJ, February 1989 SDE 3: Proceedings
of the third ACM SIGSOFT/SIGPLAN software engineering symposium on Practical
software development environments Publisher: ACM ABSTRACT “This
paper describes a software development environment based on a new approach
for managing large-scale evolving systems. Under this approach, the
conventional notion of a system is augmented with a new component called the
law of the system, which is an explicit and strictly enforced set of rules
about the operation of the system, about its evolution, and about the evolution
of the law itself. The resulting combination is called a law-governed
system.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=64135.65010&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 Also published in: SIGSOFT Software Engineering Notes, Volume 13 Issue 5, November 1988 SIGPLAN Notices, Volume 24 Issue 2, February 1989 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=65010&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 ************************************** Law-governed
software processes Rutgers University, New Brunswick, NJ, October 1990 Proceedings of the 5th international
software process workshop on Experience with software process models Publisher: IEEE Computer Society Press (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=317498.317723&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=317723&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 ************************************** Software Engineering Journal, Volume 6 , Issue 5 (September 1991) Publisher: Michael Faraday House Herts, UK, UK (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=137657.137662&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 ************************************************************************* A system for
automatic Cobol program documentation Vicente
Lopez Trueba, Julio
Cesar Leon Carrillo, Oscar
Olvera Posadas, Carlos
Ortega Hurtado, May 1984 SIGDOC '84: Proceedings of the 3rd annual international conference on Systems
documentation Publisher:ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=800018.800551&coll=ACM&dl=ACM&CFID=45955737&CFTOKEN=91858062 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=800551&type=pdf&coll=ACM&dl=ACM&CFID=45955737&CFTOKEN=91858062 ************************************************************************* What Hides in
Dimension X? A Quest for Visualizing Particle Swarms Namrata
Khemka, Christian Jacob, September 2008 Evolutionary and Swarm Design Group,
Dept. of Computer Science, University of Calgary, Alberta, Canada Lecture Notes In Computer Science; Vol. 5217 ANTS '08: Proceedings of the 6th international conference on Ant Colony Optimization
and Swarm Intelligence Section: A Combined Ant Colony and
Differential Evolution Feature Selection Algorithm Pages: 191 – 202, Brussels, Belgium,
Year of Publication: 2008 Publisher: Springer-Verlag Berlin, Heidelberg ABSTRACT “The way
we perform evolutionary experiments is all influenced by visualizing
multi-dimensional solutions, analyzing the extent to which the search space
is explored, displaying the gross population statistics, determining
clustering and building blocks, and finding successful combinations of
parameter values. Through visualization we can gain valuable insights to
enhance our knowledge about particle swarm optimizers, in particular, and the
search space that is being explored. In this paper, we focus on different visualization
techniques for particle swarm systems. We investigate the advantages of a
range of graphical data representation methods by example of the two- and
four-dimensional sphere function, the two-dimensional simplified foxholes
function, and a 56-dimensional real-world example in the context of muscle
stimulus patterns.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1432236.1432254&coll=ACM&dl=ACM&CFID=47460721&CFTOKEN=36888833 ************************************************************************* SwarmArt:
interactive art from swarm intelligence Jeffrey E. Boyd, Gerald Hushlak,
Christian J. Jacob, October 2004 MULTIMEDIA '04: Proceedings of the 12th annual ACM international conference on
Multimedia Publisher: ACM “This paper describes
<i>SwarmArt</i>, a collaborative project between computer science
and art, which resulted in two installations of interactive art that
incorporates swarm intelligence. We describe the scientific context of the
artwork, technical ...” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1027527.1027674&coll=ACM&dl=ACM&CFID=47460721&CFTOKEN=36888833 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1027674&type=pdf&coll=ACM&dl=ACM&CFID=47460721&CFTOKEN=36888833 ************************************************************************* Distributed
systems and self-organization June Power, January 1990 CSC '90: Proceedings of the 1990 ACM annual conference on Cooperation Publisher: ACM “This paper examines distributed systems
as complex systems which need to evolve in order to adapt to unpredictable
events. As a consequence, system management must concern itself not only with
changing resources, but also with the way information changes...” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=100348.100406&coll=ACM&dl=ACM&CFID=46865330&CFTOKEN=32004224 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=100406&type=pdf&coll=ACM&dl=ACM&CFID=46865330&CFTOKEN=32004224 ************************************************************************* Lee Spector, July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM “This tutorial provides an introduction
to quantum computing and to the use of evolutionary computation for automatic
quantum computer programming. No prior experience with quantum mechanics or
with evolutionary computation will be assumed. While the ...” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1274000.1274128&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1274128&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 ************************************************** Evolving quantum computer algorithms Lee Spector, Hampshire College, Amherst, MA, USA Genetic
And Evolutionary Computation Conference Proceedings of the 11th Annual
Conference Companion on Genetic and Evolutionary Computation Conference: Late
Breaking Papers, Montreal,
Québec, Canada TUTORIAL SESSION: Tutorials, Pages 3287-3316, Year of Publication: 2009 Sponsors SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation Publisher ACM New York, NY, USA ABSTRACT “Computer science will be radically
transformed if ongoing efforts to build large-scale quantum computers
eventually succeed and if the properties of these computers meet optimistic
expectations. Nevertheless, computer scientists still lack a thorough
understanding of the power of quantum computing, and it is not always clear
how best to utilize the power that is understood. This dilemma exists because
quantum algorithms are difficult to grasp and even more difficult to write.
Despite large-scale international efforts, only a few important quantum
algorithms are documented, leaving many essential questions about the
potential of quantum algorithms unanswered. These unsolved problems are ideal
challenges for the application of automatic programming technologies. Genetic
programming techniques, in particular, have already produced several new
quantum algorithms and it is reasonable to expect further discoveries in the
future. These methods will help researchers to
discover how additional practical problems can be solved using quantum
computers, and they will also help to guide theoretical work on both the
power and limits of quantum computing. This tutorial will provide an
introduction to quantum computing and an introduction to the use of
evolutionary computation for automatic quantum computer programming. No
background in physics or in evolutionary computation will be assumed. While
the primary focus of the tutorial will be on general concepts, specific
results will also be presented, including human-competitive results produced
by genetic programming. Follow-up material is available from the presenter's
book, Automatic Quantum Computer Programming: A Genetic Programming Approach,
published by Springer and Kluwer Academic Publishers.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1570256.1570420 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1570420&type=pdf&coll=GUIDE&dl=GUIDE&CFID=69316565&CFTOKEN=79704043 ************************************************************************* Eden: An
Evolutionary Sonic Ecosystem Jon McCormack, September 2001 Lecture Notes In Computer Science; Vol. 2159 ECAL '01: Proceedings of the 6th European Conference on Advances in Artificial
Life Publisher Springer-Verlag London, UK (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=645302.648830&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Eden: An Evolutionary Sonic Ecosystem Jon McCormack “eden is an interactive, self-generating, artificial
ecosystem. A cellular world is populated by collections of evolving virtual creatures.
Creatures move about the environment, making and listening to sounds,
foraging for food, encountering predators and possibly mating with each
other. Over time, creatures evolve to fit their landscape. eden has four seasons per year and each year lasts 600
eden days. One eden year passes by in about fifteen minutes of real time. A
simple physics dictates only three basic types of matter in the Eden world: rocks,
biomass and sonic animals.” (FGS Link, July, 2010) http://www.csse.monash.edu.au/~jonmc/projects/eden/ http://www.csse.monash.edu.au/~jonmc/projects/eden/eden.html *********************************** New challenges
for evolutionary music and art Jon McCormack, Monash University, Australia, April
2006 ACM SIGEVOlution, Volume 1 , Issue 1 (April 2006), Pages: 5 – 11, 2006 Publisher: ACM ABSTRACT “Art, it
was once said, is anything you can get away with. So it is not surprising
that evolutionary approaches to music and art research are challenging our
notions of what is classified as "Art" and who is the
"creator" of this work. The relatively new field of Evolutionary
Music and Art (EMA) falls within the spectrum of Evolutionary Computing. If
EC is a relatively young discipline, then EMA is even more so, if we consider
Richard Dawkins' "Blind Watchmaker" software (1986) as the epoch in
this field.1 Dawkins' goal was to demonstrate the power of evolution as a
design algorithm, one that could design complexity without the need for an
explicit designer. It did not take long for people interested in creativity
and aesthetics to grasp the significance of this idea and how it might be
used to create a new class of art and design: one that was evolved rather
than directly created.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1138470.1138472&coll=Portal&dl=GUIDE&CFID=45326681&CFTOKEN=72054038 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1138472&type=pdf&coll=Portal&dl=GUIDE&CFID=45326681&CFTOKEN=72054038 ************************************* Artificial
ecosystems for creative discovery Jon McCormack, July 2007 Genetic
And Evolutionary Computation Conference SESSION: Artificial life, evolutionary
robotics, adaptive behavior, evolvable hardware: papers, Pages: 301 – 307, Year of
Publication: 2007 Publisher: ACM ABSTRACT “This
paper discusses the concept of an artificial ecosystem for use in
machine-assisted creative discovery. Properties and processes from natural
ecosystems are abstracted and applied to the design of creative systems, in a
similar way that evolutionary computing methods use the metaphor of Darwinian
evolution to solve problems in search and optimisation. The paper examines
some appropriate mechanisms and metaphors when applying artificial ecosystems
to problems in creative design. General properties and processes of
evolutionary artificial ecosystems are presented as a basis for developing
individual systems that automate the discovery of novelty without explicit
teleological goals. The adaptation of species to fit their environment drives
the creative solutions, so the role of the designer shifts to the design of
environments. This allows a variety of creative solutions to emerge in
simulation without the need for explicit or human-evaluated fitness measures,
such as those used in interactive evolution. Two example creative ecosystems
are described to highlight the effectiveness of the method presented.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1276958.1277017&coll=Portal&dl=GUIDE&CFID=45326681&CFTOKEN=72054038 Full text
available for ACM Digital Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1277017&type=pdf&coll=Portal&dl=GUIDE&CFID=45326681&CFTOKEN=72054038 **************************************** Life's What
You Make: Niche Construction and Evolutionary Art Jon Mccormack, Oliver Bown, April 2009 Centre for Electronic Media Art, Monash
University, Clayton, Victoria, Australia 3800 Lecture Notes In Computer Science; Vol. 5484 Proceedings of the EvoWorkshops
2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT,
EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM,
EvoSTOC, EvoTRANSLOG Section: EvoMUSART Contributions, Pages: 528 – 537, Tübingen, Germany, 2009 Publisher: Springer-Verlag, Berlin, Heidelberg ABSTRACT
“This
paper advances new methods for ecosystemic approaches to evolutionary music
and art. We explore the biological concept of the niche and its role in
evolutionary dynamics, applying it to creative computational systems. Using
the process of niche construction organisms are able to change and adapt
their environment, and potentially that of other species. Constructed niches
may become heritable environments for offspring, paralleling the way genes
are passed from parent to child. In a creative ecosystem, niche construction
can be used by agents to increase the diversity and heterogeneity of their
output. We illustrate the usefulness of this technique by applying niche
construction to line drawing and music composition.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1533570.1533637&coll=Portal&dl=GUIDE&CFID=45326681&CFTOKEN=72054038 Serie del libros Lecture Notes in Computer Science Editor Springer Berlin / Heidelberg Libro Applications of Evolutionary
Computing Páginas
528-537 Subject
Collection Informática Fecha
de SpringerLink sábado, 11 de abril de 2009 (FGS Link, July, 2009) http://www.springerlink.com/content/j604803h56337485/ Life’s
What You Make: Niche Construction and Evolutionary Art Jon McCormack and Oliver Bown Centre for Electronic
Media Art Monash University,
Clayton, Victoria 3800, Australia (FGS Link, July, 2009) http://www.csse.monash.edu.au/~jonmc/research/Papers/McCormack_EvoMUSART09.pdf ******************************** Jon McCormack :: research publications research
publications by year Most papers can
be downloaded here, where copyright permits (use the [pdf] link). (FGS Link, July, 2009) http://www.csse.monash.edu.au/~jonmc/research/publications.html ************************************************************************* Knowledge
Extraction based on Evolutionary Learning “KEEL is
a software tool to assess evolutionary algorithms for Data Mining problems
including regression, classification, clustering, pattern mining and so on.
It contains a big collection of classical knowledge extraction algorithms,
preprocessing techniques (training set selection, feature selection,
discretization, imputation methods for missing values, etc.), Computational
Intelligence based learning algorithms, including evolutionary rule learning
algorithms based on different approaches (Pittsburgh, Michigan and IRL, ...),
and hybrid models such as genetic fuzzy systems, evolutionary neural
networks, etc. It allows us to perform a complete analysis of any learning
model in comparison to existing ones, including a statistical test module for
comparison. Moreover, KEEL has been designed with a double goal: research and
educational.” (FGS Link, July, 2010) http://sci2s.ugr.es/keel/index.php Knowledge
Extraction based on Evolutionary Learning Publications (FGS Link, July, 2010) http://sci2s.ugr.es/keel/publication.php?type=thesis ************************************ Nuevos Modelos de Redes Neuronales Evolutivas y Regresión Logística Generalizada utilizando Funciones de Base. Aplicaciones. Pedro Antonio Gutiérrez Peña Tesis Doctoral Universidad de Granada, Departamento de
Ciencias de la Computación e Inteligencia Artificial, Granada, Junio 2009 http://hera.ugr.es/tesisugr/1805223x.pdf ************************************************************************* Using APL2 to
compute the dimension of a fractal represented as a grammar Manuel Alfonseca, Alfonso Ortega Universidad
Autónoma de Madrid, July 2000 APL '00: Proceedings of the international conference on APL-Berlin-2000
conference Berlin, Germany Publisher: ACM ABSTRACT “In this
paper we describe the use of APL2 to implement and depict the equivalence between
the mathematical field of fractal curves and the linguistic field of parallel
derivation grammars, by tackling the problem of determining the dimension of
a fractal from its representation as a grammar. APL2 makes the required
computation quite easy.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=570475.570477&coll=ACM&dl=ACM&CFID=47353934&CFTOKEN=22785683 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=570477&type=pdf&coll=ACM&dl=ACM&CFID=47353934&CFTOKEN=22785683 Also published in: June 2000 SIGAPL APL Quote Quad Volume 30 Issue 4 ************************************ Complex
systems in APL: fractals, evolving cellular automata and artificial life Universidad
Autónoma de Madrid Universidad
Autónoma de Madrid CIEMAT July 2002 APL '02: Proceedings of the 2002 conference on APL: array processing languages:
lore, problems, and applications, Madrid, Spain Publisher: ACM ABSTRACT “We have
been working for several years on the representation, study and simulation of
complex systems by means of formal methods. APL2 and other programming
languages have been used to develop our tools and experiments. This paper
summarizes our APL2 works on fractal sets, automatic programming of cellular
automata and simulation of multi agent systems.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=602231.602233&coll=ACM&dl=ACM&CFID=47353934&CFTOKEN=22785683 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=602233&type=pdf&coll=ACM&dl=ACM&CFID=47353934&CFTOKEN=22785683 Also published in: SIGAPL APL Quote Quad, Volume 32 Issue 4, June 2002 ************************************************************************* Automatic
composition of music by means of grammatical evolution Universidad
Autónoma de Madrid Universidad
Autónoma de Madrid & IBM Universidad
Autónoma de Madrid July 2002 APL '02: Proceedings of the 2002 conference on APL: array processing languages:
lore, problems, and applications, Madrid, Spain Publisher: ACM ABSTRACT “This
work describes how grammatical evolution may be applied to the domain of
automatic composition. Our goal is to test this technique as an alternate
tool for automatic composition. The AP440 auxiliary processor will be used to
play music, thus we shall use a grammar that generates AP440 melodies.
Grammar evolution will use fitness functions defined from several well-known
single melodies to automatically generate AP440 compositions that are
expected to sound like those composed by human musicians.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=602231.602249&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=602249&type=pdf&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Also published in: June 2002 SIGAPL APL Quote Quad Volume 32 Issue 4 PDF http://arantxa.ii.uam.es/~alfonsec/artint/apl2002b.pdf ************************************ Grammatical
evolution to design fractal curves with a given dimension A. Ortega, A. Dalhoum,
M. Alfonseca
Universidad
Autónoma de Madrid, Campus de Cantoblanco, 28049 Madrid, Spain July 2003 IBM Journal of Research and Development , Volume 47 Issue 4, July 2003 Publisher: IBM Corp. ABSTRACT “Lindenmayer
grammars have frequently been applied to represent fractal curves. In this
work, the ideas behind grammar evolution are used to automatically generate
and evolve Lindenmayer grammars which represent fractal curves with a fractal
dimension that approximates a predefined required value. For many dimensions,
this is a nontrivial task to be performed manually. The procedure we propose
closely parallels biological evolution because it acts through three
different levels: a genotype (a vector of integers), a protein-like
intermediate level (the Lindenmayer grammar), and a phenotype (the fractal
curve). Variation acts at the genotype level, while selection is performed at
the phenotype level (by comparing the dimensions of the fractal curves to the
desired value).” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1014587.1014594&coll=ACM&dl=ACM&CFID=47353934&CFTOKEN=22785683 ************************************ Automatic
generation of benchmarks for plagiarism detection tools using grammatical
evolution
Manuel Cebrián, Manuel Alfonseca, Alfonso Ortega Universidad
Autónoma de Madrid, Madrid, Spain, July 2007 GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary
computation Publisher: ACM ABSTRACT “Student
plagiarism is a mayor problem in universities worldwide. In this paper,we focus
on plagiarism in answers to computer programming assignments,where student
mix and/or modify one or more original solutions to obtain counterfeits.
Although several software tools have been implemented to help the tedious and
time consuming task of detecting plagiarism, little has been done to assess
their quality, because, in fact, determining the original subset of the whole
solution set is practically impossible for graders. In this article we
present a Grammatical Evolution technique which generates benchmarks. Given a
programming language, our technique generates a set of original solutions to
an assignment, together with a set of plagiarisms of the former set which
mimic the way in which students act. The phylogeny of the coded solutions is
predefined, providing a base for the evaluation of the performance of
copy-catching tools. We give empirical evidence of the suitability of our
approach by studying the behavior of one state-of-the-art detection tool (AC)
on four benchmarks coded in APL2, generated with this technique.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1276958.1277388&coll=ACM&dl=ACM&CFID=45851609&CFTOKEN=24619111 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1277388&type=pdf&coll=ACM&dl=ACM&CFID=45851609&CFTOKEN=24619111 ************************************************************************* Teaching
Machines about Everyday Life P. Singh, B. Barry, H. Liu, October 2004 BT Technology Journal, Volume 22 , Issue 4 (October 2004), Pages:
227 - 240 Publisher: Kluwer Academic
Publishers Hingham, MA, USA ABSTRACT “In order
to build software that can deeply understand people and our problems, we
require computational tools that give machines the capacity to learn and
reason about everyday life. We describe three commonsense knowledge bases
that take unconventional approaches to representing, acquiring, and reasoning
with large quantities of commonsense knowledge. Each adopts a different
approach — ConceptNet is a large-scale semantic network, LifeNet is a
probabilistic graphical model, and StoryNet is a database of story-scripts.
We describe the evolution, architecture and operation of these three systems,
and conclude with a discussion of how we might combine them into an
integrated commonsense reasoning system.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1031374 PDF http://larifari.org/writing/BTTJ2004-EverydayLife.pdf ************************************************************************* Nonlinear
dynamics modelling for controller evolution Julian Togelius, Renzo De Nardi, Hugo Marques, Richard Newcombe, Simon M. Lucas, Owen Holland University of Essex, Colchester, United
Kingdom, July 2007 GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary
computation Publisher: ACM ABSTRACT “The
problem of how to acquire a model of a physical robot, which is fit for
evolution of controllers that can subsequently be used to control that robot,
is considered in the context of racing a radio-controlled toy car around a
randomised track. Several modelling techniques are compared, and the specific
properties of the acquired models that influence the quality of the evolved
controller are discussed. As we aim to minimise the amount of domain
knowledge used, we further investigate the relation between the assumptions
about the modelled system made by particular modelling techniques and the
suitability of the acquired models as bases for controller evolution. We find
that none of the models acquired is good enough on its own, and that a key to
evolving robust behaviour is to evaluate controllers simultaneously on
multiple models during evolution. Examples of successfully evolved racing
control for the physical car are analysed.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1276958.1277020&coll=ACM&dl=ACM&CFID=45819817&CFTOKEN=43501540 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1277020&type=pdf&coll=ACM&dl=ACM&CFID=45819817&CFTOKEN=43501540 ************************************************************************* Intrinsic
evolution of digital circuits using evolutionary algorithms Guoliang He, Yuanxiang Li, Zhongzhi Shi, Ting Hu June 2009 GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary
Computation Publisher: ACM ABSTRACT “Currently, the
auto-design of electronic and analog circuits is an intensively studied topic
in the field of evolvable hardware. In order to improve evolutionary design
of logic circuits in efficiency, capability of optimization and safety of
on-line evolution, an elitist pool evolutionary algorithm (EPEA) based on
novel approaches is proposed. First, an extended matrix encoding method is
devised, which can be able to reflect the potential performance of a circuit
and reduce the risk of deleting a circuit with a good developing potential
during evolution. Then, a novel sub-circuit crossover operator and an
adaptive mutation strategy are introduced to improve the local optimization
and maintain the diversity of a population in the evolution. At last, a
framework of on-line evolution is used to implement EPEA on a
field-programmable gate array. Experiments show that our proposed method is
able to design valid and novel circuits efficiently.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1543834.1543863&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1543863&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 ************************************************************************* CG-1, a
course generating program for computer-assisted instruction Charles T Meadow, Douglas W Waugh, Forrest E Miller January 1968 Proceedings of the 1968 23rd ACM
national conference, Pages: 99 - 110 Publisher: ACM ABSTRACT “In this
paper we discuss a computer program which assists authors in producing
computer-assisted instruction courses. The courses are executable computer
programs. The course generator, CG-1, is an interactive program which
produces a course program as the result of a conversation between the
computer and the course author carried on entirely in natural language. The
generated programs are in a language called the PL/I Interactive Dialect
(PL/I ID) which is derived from PL/I. CG-1 was written in a form of the
interactive dialect. The principal concepts and initial programming for this
project originated during a design study of a larger system 1 which had as
its objective the elicitation and dissemination of computer program
documentation. From various other modules of the system, we were able to
assume the availability of natural language documentation in machine readable
form, and we were required to develop a method of converting this information
into instruction courses. Instructional materials were to be aimed primarily
at professional programmers joining a large, existing project, or users of a
large program system who were not programmers,” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=800186.810572&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=810572&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 ************************************************************************* Evolutionary
design of complex software (EDCS) ARPA ITO, SIGSOFT Software Engineering Notes , Volume 20 Issue 5, December 1995 Publisher: ACM ABSTRACT “This
document is intended to provide background information for offerers
responding to BAA 95-40: Evolutionary Design of Complex Software (EDCS). It
describes the general problem that the EDCS Program addresses along with some
of the characteristics of the Program's organization. Then it discusses some
concepts for evolutionary systems and some of the technology investigations
that are felt to support those concepts. This is presented for illustrative
purposes only. Offerors should not feel constrained to the particular
concepts of evolution articulated nor to the technology areas and sample
investigation projects delineated.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=217030.217033&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=217033&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 ************************************************************************* TalkMine and
the adaptive recommendation project Complex
Systems Modeling Team, Computer Research and Applications Group, Los Alamos
National Laboratory, MS B265, Los Alamos, NM, August
1999 DL '99: Proceedings of the fourth ACM conference on Digital libraries Publisher: ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=313238.313416&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=313416&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 ************************************************************************* An
architecture for constructing self-evolving software systems Chrysanthos Dellarocas, Mark Klein,
Howard Shrobe Massachusetts
Institute of Technology, Cambridge, MA, November
1998 ISAW '98: Proceedings of the third international workshop on Software
architecture, 1998 Publisher: ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=288408.288416&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=288416&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 ************************************************************************* Self-Adaptive
Software:Landscape and Research Challenges Mazeiar
Salehie, Ladan
Tahvildari University of Waterloo, Waterloo, Canada ACM Transactions on Autonomous and Adaptive Systems
(TAAS) Volume 4 ,
Issue 2 (May 2009), Article No.: 14, 2009 ACM New York, NY, USA ABSTRACT “Software systems dealing with distributed
applications in changing environments normally require human supervision to
continue operation in all conditions. These (re-)configuring,
troubleshooting, and in general maintenance tasks lead to costly and
time-consuming procedures during the operating phase. These problems are
primarily due to the open-loop structure often followed in software
development. Therefore, there is a high demand for management complexity
reduction, management automation, robustness, and achieving all of the desired
quality requirements within a reasonable cost and time range during
operation. Self-adaptive software is a response to these demands; it is a
closed-loop system with a feedback loop aiming to adjust itself to changes
during its operation. These changes may stem from the software system's self
(internal causes, e.g., failure) or context (external events, e.g.,
increasing requests from users). Such a system is required to monitor
itself and its context, detect significant changes, decide how
to react, and act to execute such decisions. These processes depend on
adaptation properties (called self-* properties), domain
characteristics (context information or models), and preferences of
stakeholders. Noting these requirements, it is widely believed that new models
and frameworks are needed to design self-adaptive software. This survey
article presents a taxonomy, based on concerns of adaptation, that is, how,
what, when and where, towards providing a unified view
of this emerging area. Moreover, as adaptive systems are encountered in many
disciplines, it is imperative to learn from the theories and models developed
in these other areas. This survey article presents a landscape of research in
self-adaptive software by highlighting relevant disciplines and some prominent
research projects. This landscape helps to identify the underlying research
gaps and elaborates on the corresponding challenges.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1516533.1516538 Full text available for ACM Digital Library
Members:
http://portal.acm.org/ft_gateway.cfm?id=1516538&type=pdf&coll=GUIDE&dl=GUIDE&CFID=95615457&CFTOKEN=71105605 ************************************************************************* Book Creative
evolutionary systems Editors
Univ. College London, London, UK Univ. of Reading, Reading, UK Publisher Morgan Kaufmann Publishers Inc. San Francisco, CA, USA Pages: 576, Year of Publication: 2001 ABSTRACT “The use of evolution
for creative problem solving is one of the most exciting and potentially
significant areas in computer science today. Evolutionary computation is a
way of solving problems, or generating designs, using mechanisms derived from
natural evolution. This book concentrates on applying important ideas in
evolutionary computation to creative areas, such as art, music, architecture,
and desgn. It shows how human interaction, new representations, and
approaches such as open-ended evolution can extend the capabiities of
evolutionary computation from optimization of existing solutions to
innovation and the generation of entirely new and original solutions. This
book takes a fresh look at creativity, exploring what it is and how the
actions of evolution can resemble it. Examples of novel evolved solutions are
presented in a variety of creative disciplines. The editors have compiled
contributions by leading researchers in each discipline.” (FGS Link, July, 2010) http://portal.acm.org/citation.cfm?id=510349&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 *********************************** A remark on
evolutionary systems Computer and Automation Research Institute,
Hungarian Academy of Sciences, Kende utca 13-17, H-1111 Budapest, Hungary Fakultät für Informatik
Otto-von-Guericke-Universität Magdeburg, PSF 4120, D-39016 Magdeburg, Germany Discrete Applied Mathematics , Volume 146 Issue 1, February 2005 Publisher: Elsevier Science Publishers
B. V., Amsterdam, The
Netherlands ABSTRACT Evolutionary
systems have been introduced by Csuhaj-Varjú and Mitrana (Acta Inform. 36
(2000) 913) who proved that two context-sensitive or three context-free
components are sufficient to obtain all recursively enumerable languages. We
improve these results by showing that two context-free components are
sufficient to generate all recursively enumerable languages. Furthermore, we
study the power of systems with one component. (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1080499.1080511&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 ************************************************************************* ************************************************************************* ************************************************************************* Evolving
images for entertainment Qinying Xu, Daryl D'Souza, Vic Ciesielski December 2007 IE '07: Proceedings of the 4th Australasian conference on Interactive
entertainment Publisher: RMIT University Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1367982&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 “Images are widely used in media
contexts such as web design, games and video animation. The process of
creating interesting images can be enjoyable if a useful tool is involved. In
this paper we describe an interactive image generation tool called IMAGENE
...” http://delivery.acm.org/10.1145/1370000/1367982/a26-xu.pdf?key1=1367982&key2=2426909421&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 (FGS Link, July, 2009) ************************************************************************* September 2007 SIGEVOlution , Volume 2 Issue 3 Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1366916&type=pdf&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 “The most interesting results in
Artificial Life come about when some aspect of reality is captured. In the
mid-1990s, Karl Sims energized the AL community with his ground-breaking work
on evolved moving creatures [28, 29]. The life-like behavior of Sims' ...” http://delivery.acm.org/10.1145/1370000/1366916/p6-funes.pdf?key1=1366916&key2=4486909421&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 (FGS Link, July, 2009) ************************************************************************* Implementation
issues for an interactive evolutionary computation system July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1274100&type=pdf&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 The design and development of an
Interactive Evolutionary Computation (IEC) system needs to take into account
the implementation issues found when delivering the system to "Real
World" users. This paper reports and reflects on the implementation
issues ... http://delivery.acm.org/10.1145/1280000/1274100/p2933-shackelford.pdf?key1=1274100&key2=7239619421&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* Evolutionary
system development Peter J. Denning, Chris Gunderson, Rick Hayes-Roth December 2008 Communications of the ACM , Volume 51 Issue 12 Publisher: ACM Full text available for ACM Digital
Library Members: http://portal.acm.org/ft_gateway.cfm?id=1409371&type=digital
edition&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706
http://portal.acm.org/ft_gateway.cfm?id=1409371&type=html&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706
http://portal.acm.org/ft_gateway.cfm?id=1409371&type=pdf&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 ABSTRACT “Large systems projects are failing at
an alarming rate. It's time to take evolutionary design methods off the
shelf.” http://delivery.acm.org/10.1145/1410000/1409371/p29-denning.pdf?key1=1409371&key2=4639619421&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* Learning
classifier systems and evolutionary robotics May 2005 Journal of Computing Sciences in
Colleges , Volume
20 Issue 5 Publisher: Consortium for Computing Sciences in Colleges Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1059913&type=pdf&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 “My research has been in the domain of
mobile robots with Learning Classifier System (LCS) controllers. LCSs[1]
utilize reinforcement learning and a genetic algorithm to evolve a set of
condition-action rules, or classifiers. If a particular condition ...” http://delivery.acm.org/10.1145/1060000/1059913/p112-smith.pdf?key1=1059913&key2=3520719421&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* RCS: a
learning classifier system for evolutionary robotics Noah W. Smith, Clare Bates Congdon June 2005 GECCO '05: Proceedings of the 2005 workshops on Genetic and evolutionary
computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1102283&type=pdf&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 “This paper introduces RCS, a learning
classifier system designed for evolutionary robotics research. In addition to
describing the system, it will present the results of RCS applied to a
pursuit task. In this test, performance was good and has been improved ...” http://delivery.acm.org/10.1145/1110000/1102283/p119-smith.pdf?key1=1102283&key2=4750719421&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* Towards
engineered architecture evolution Sagar Chaki, Andres Diaz-Pace, David
Garlan, Arie Gurfinkel, Ipek Ozkaya May 2009 MISE '09: Proceedings of the 2009 ICSE Workshop on Modeling in Software
Engineering Publisher: IEEE Computer Society Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1564697&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 “Architecture evolution, a key aspect of
software evolution, is typically done in an ad hoc manner, guided only by the
competence of the architect performing it. This process lacks the rigor of an
engineering discipline. In this paper, we argue that architecture ...” http://delivery.acm.org/10.1145/1570000/1564697/05069889.pdf?key1=1564697&key2=7663819421&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* July 1999 Autonomous Robots , Volume 7 Issue 1 Publisher: Kluwer Academic Publishers “In the last few years several
researchers have resorted to artificial evolution (e.g., genetic algorithms)
and learning techniques (e.g., neural networks) for studying the interaction
between learning and evolution. These studies have been conducted ...” (FGS Link, July, 2009) ************************************************************************* The influence
of learning on evolution Domenico
Parisi, Stefano Nolfi April 1996 Adaptive individuals in evolving
populations Publisher: Addison-Wesley Longman Publishing Co., Inc. (FGS Link, July, 2009) ************************************************************************* Learning and
evolution in neural networks Stefano
Nolfi, Domenico Parisi, Jeffrey L. Elman June 1994 Adaptive Behavior , Volume 3 Issue 1 Publisher: MIT Press (FGS Link, July, 2009) ************************************************************************* July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1274119&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 “Neuroevolution, i.e. evolution of
artificial neural networks, has recently emerged as a powerful technique for
solving challenging reinforcement learning problems. Compared to
traditional(e.g. value-function based) methods, neuroevolution is especially
...” http://delivery.acm.org/10.1145/1280000/1274119/p3415-miikkulainen.pdf?key1=1274119&key2=8988819421&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ****************************** Risto Miikkulainen, Kenneth O. Stanley July 2008 GECCO '08: Proceedings of the 2008 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1389080&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 “Neuroevolution, i.e. evolution of
artificial neural networks, has recently emerged as a powerful technique for
solving challenging reinforcement learning problems. Compared to traditional
(e.g. value-function based) methods, neuroevolution is especially ...” http://delivery.acm.org/10.1145/1390000/1389080/p2829-miikkulainen.pdf?key1=1389080&key2=1309819421&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ****************************** Evolving
neural networks through augmenting topologies Kenneth O. Stanley, Risto Miikkulainen June 2002 Evolutionary Computation , Volume 10 Issue 2 Publisher: MIT Press “An important question in neuroevolution
is how to gain an advantage from evolving neural network topologies along
with weights. We present a method, NeuroEvolution of Augmenting Topologies
(NEAT), which outperforms the best fixed-topology method on a ...” http://portal.acm.org/citation.cfm?id=638553.638554&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* D. B. Fogel, L. J. Fogel, V. W. Porto September 1990 Biological Cybernetics , Volume 63 Issue 6 Publisher: Springer-Verlag New York, Inc. (FGS Link, July, 2009) ************************************************************************* David B. Fogel January 2006 Evolutionary Computation: Toward a New
Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
Publisher: Wiley-IEEE Press (FGS Link, July, 2009) ************************************************************************* David B. Fogel November 2005 Evolutionary Computation Publisher: John Wiley & Sons (FGS Link, July, 2009) ************************************************************************* Quantum
Computing A Tutorial at the 2003 Genetic and Evolutionary Computation Conference
(GECCO-2003) Lee Spector, School of Cognitive Science, Hampshire College,
Amherst, MA 01002, USA Includes results of collaborations with Herbert J. Bernstein, Howard Barnum, and Nikhil Swamy. http://hampshire.edu/lspector/GECCO-2003-Tutorial.htm (FGS Link, July, 2009) ************************************************************************* July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1274127&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 “In this tutorial, fundamental concepts
of evolutionary circuit design and evolvable hardware will be introduced. In
particular, we will deal with evolutionary algorithms and reconfigurable
devices utilized for hardware evolution as well as with their ...” http://delivery.acm.org/10.1145/1280000/1274127/p3627-sekanina.pdf?key1=1274127&key2=3271919421&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* Baldwin
effect “The Baldwin
effect, also known as Baldwinian evolution or ontogenic
evolution, is an early evolutionary theory put
forward in 1896 in a paper "A New Factor in Evolution" by American
psychologist
James Mark Baldwin which proposes a mechanism
for specific selection for general learning ability. Selected offspring would
tend to have an increased capacity for learning new skills rather than being
confined to genetically coded, relatively fixed abilities. In effect, it
places emphasis on the fact that the sustained behavior of a species or group
can shape the evolution of that species. The "Baldwin effect" is
better understood in evo-devo literature as a scenario in which a character or trait
change occurring in an organism as a result of its interaction with its
environment becomes gradually assimilated into its developmental
genetic/epigenetic repertoire (Simpson, 1953; Newman, 2002).” (Wikipedia
August 1, 2009) http://en.wikipedia.org/wiki/Baldwin_effect Efecto Baldwin “El efecto Baldwin. también llamado evolución baldwiniana u ontogenética es una teoría evolutiva propuesta en 1896 por el psicólogo norteamericano James Mark Baldwin, quien propuso un mecanismo para la selección de habilidades de aprendizaje. La descendencia seleccionada tendería hacia una mayor capacidad para aprender nuevas habilidades y no estar constreñida a habilidades genéticamente codificadas y relativamente fijas, poniendo el énfasis en el hecho de que el comportamiento sostenido de una especie o grupo puede modelar la evolución de las especies.”(Wikipedia, 1 de Agosto, 2009) http://es.wikipedia.org/wiki/Efecto_Baldwin ************************************ The influence
of learning on evolution: A mathematical framework Ingo Paenke, Tadeusz J. Kawecki, Bernhard Sendhoff April 2009 Artificial Life , Volume 15 Issue 2 Publisher: MIT Press “The Baldwin effect can be
observed if phenotypic learning influences the evolutionary fitness of
individuals, which can in turn accelerate or decelerate evolutionary change.
Evidence for both learning-induced acceleration and deceleration can ...” (FGS Link, July, 2009) ************************************************************************* Introductory
tutorial on coevolution Edwin D. de Jong, Kenneth O. Stanley, R. Paul Wiegand July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1274108&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 “This tutorial is designed to introduce coevolution
to those with a working familiarity with evolutionary computation. The
tutorial begins by providing some basic background into what coevolution is
and how it has been historically employed. The fundamental ...” http://delivery.acm.org/10.1145/1280000/1274108/p3133-de-jong.pdf?key1=1274108&key2=2054919421&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* Advanced
tutorial on coevolution Sevan G. Ficici, Anthony Bucci July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM Full text available for ACM Digital
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http://portal.acm.org/ft_gateway.cfm?id=1274110&type=pdf&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 The advanced tutorial on coevolution continues
the topics covered in the introductory coevolution tutorial with a view
towards research conducted in the last eight years. We will explore two
themes which have recently been identified: interaction, which ... http://delivery.acm.org/10.1145/1280000/1274110/p3172-ficici.pdf?key1=1274110&key2=1654919421&coll=GUIDE&dl=GUIDE&CFID=46014229&CFTOKEN=26545706 (FGS Link, July, 2009) ************************************************************************* Evolutionary
design of dynamic SwarmScapes Namrata Khemka, Scott Novakowski, Gerald
Hushlak, Christian Jacob July 2008 GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary
computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1389257&type=pdf&coll=ACM&dl=ACM&CFID=47460721&CFTOKEN=36888833 “This paper discusses interactive
evolutionary algorithms and their application in swarm-based image
generation. From an artist's perspective, the computer-generated patterns
offer departure points for creative "Imagineering." Input into our
evolutionary ...” http://delivery.acm.org/10.1145/1390000/1389257/p827-khemka.pdf?key1=1389257&key2=1092439421&coll=ACM&dl=ACM&CFID=47460721&CFTOKEN=36888833 (FGS Link, July, 2009) ************************************************************************* Interactive
Evolutionary Evaluation through Spatial Partitioning of Fitness Zones Namrata Khemka, Gerald Hushlak,
Christian Jacob April 2009 EvoWorkshops '09: Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary
Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP,
EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG Publisher: Springer-Verlag “This paper discusses how large-scale
interactive evolutionary design can be accomplished through innovative
evaluation interfaces. An application example from the world of textile
designing and fine arts serves to illustrate an evolutionary evaluation ...” (FGS Link, July, 2009) ************************************************************************* Metrics for Phylogenetic Networks I: Generalizations of the
Robinson-Foulds Metric Full text
http://portal.acm.org/ft_gateway.cfm?id=1512455&type=pdf&coll=portal&dl=ACM&CFID=515651565&CFTOKEN=515651565 Source IEEE/ACM Transactions on Computational Biology and
Bioinformatics (TCBB) archive Pages 46-61 Year of Publication: 2009 ISSN:1545-5963 Authors University of the Balearic Islands,
Palma de Mallorca University of the Balearic Islands,
Palma de Mallorca University of the Balearic Islands,
Palma de Mallorca Technical University of Catalonia,
Barcelona Publisher IEEE Computer Society Press Los Alamitos, CA, USA (FGS Link, July, 2009) ************************************************************************* Metrics for Phylogenetic Networks II: Nodal and Triplets Metrics Full text
http://portal.acm.org/ft_gateway.cfm?id=1577996&type=pdf&coll=portal&dl=ACM&CFID=54861214&CFTOKEN=88881486 Source IEEE/ACM Transactions on Computational Biology and
Bioinformatics (TCBB) archive Pages 454-469 Year of Publication: 2009 ISSN:1545-5963 Authors University of the Balearic Islands,
Palma de Mallorca University of the Balearic Islands,
Palma de Mallorca University of the Balearic Islands,
Palma de Mallorca Technical University of Catalonia,
Barcelona Publisher IEEE Computer Society Press Los Alamitos, CA, USA (FGS Link, July, 2009) ************************************************************************* The impact of
network topology on self-organizing maps Fei Jiang, Hugues Berry, Marc Schoenauer June 2009 GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary
Computation Publisher: ACM Full text available for ACM Digital
Library Members:
http://portal.acm.org/ft_gateway.cfm?id=1543869&type=pdf&coll=ACM&dl=ACM&CFID=46542654&CFTOKEN=84397846 ABSTRACT “In this paper, we study instances of complex neural
networks, i.e. neural networks with complex topologies. We use
Self-Organizing Map neural networks whose neighborhood relationships are de_ned by a complex
network, to classify handwritten digits. We show that topology has a small
impact on performance and robustness to neuron failures, at least at long
learning times. Performance may however be increased (by almost 10%) by
evolutionary optimization of the network topology. In our experimental
conditions, the evolved networks are more random than their parents, but
display a more heterogeneous degree distribution.” PDF http://www.inrialpes.fr/Berry/Images/GEC09_EVVON.pdf (FGS Link, July, 2009) ************************************************************************* ************************************************************************* |
