Evolución, sistemas
evolutivos, enfoque lingüístico, lingüística matemática, reconocimiento
sintáctico de patrones, inferencia gramatical, gramáticas evolutivas,
computación evolutiva, programación genética, matrices evolutivas, redes
neuronales evolutivas, reconocimiento de patrones, autómatas celulares
evolutivos, sistemas complejos, sistemas expertos, aprendizaje de maquinas,
procesos de Markov, recursividad, complejidad, informática, sistemas
adaptativos, hardware
evolutivo.
Crecimiento, aprendizaje, pensamiento, transformación de nuestra imagen de la realidad, inteligencia artificial, vida artificial, procesos de descomposición, el desarrollo y transformación de las empresas, sociedades, organizaciones, países, galaxias y universos, vida, cambio.
Evolución y Educación, Invención por evolución, Sistemas Evolutivos y música, Robótica evolutiva, sistemas evolutivos de la naturaleza, generación de paisajes, árboles, nubes, ríos, etc..
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Evolution and Evolutionary Systems LINKS to
Approaches,
Methods and Tools Evolución y Sistemas Evolutivos LIGAS
a Enfoques,
Métodos y Herramientas www.fgalindosoria.com/eac/evolucion/
www.fgalindosoria.com/eac/
Fernando Galindo Soria
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Evolución y Sistemas Evolutivos, Sistemas Afectivos y
Sistemas Concientes
Evolución y Sistemas Evolutivos Sistemas Afectivos Sistemas Concientes
Matrices
Evolutivas y Dinámica Dimensional
Creación
de la página www Cd. De México a 2 de Junio del 2001
Ultimas actualizaciones 27 de Mayo del 2007, 9 de Diciembre del
2008, 9 de Julio del 2009, 11 de Julio del 2010
Approaches,
Methods and Tools
Enfoques,
Métodos y Herramientas
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*********************************************************** Evolutionary Informatics
/ Informática evolutiva Evolutionary
Informatics “Evolutionary
informatics merges theories of evolution and information, thereby wedding the
natural, engineering, and mathematical sciences. Evolutionary informatics
studies how evolving systems incorporate, transform, and export information.
The Evolutionary Informatics Laboratory explores the conceptual foundations,
mathematical development, and empirical application of evolutionary
informatics. The principal theme of the lab’s research is teasing apart the
respective roles of internally generated and externally applied information
in the performance of evolutionary systems.” (Link June 2, 2010) http://www.evolutionaryinformatics.org/ Informática evolutiva “Informática evolutiva es un subcampo de informática tratando la práctica de la tratamiento de la información adentro, y la ingeniería de los sistemas de información para, el estudio de evolución biológica, así como el estudio de la información en sistemas evolutivos, natural y artificial.” http://www.worldlingo.com/ma/enwiki/es/Evolutionary_informatics Future IT: A
Look at How It Will Evolve By Michael
Fitzgerald, CIO, December 15, 2003 “Evolution is a hot topic in IT circles. There
is, appropriately enough, evolutionary computation, which bases aspects of
computing on biological systems that gradually change into "a different
and usually more complex or better form." The process of evolution
provides models for dealing with the complexity of advanced IT systems.” http://www.cio.com/article/32037/Future_IT_A_Look_at_How_It_Will_Evolve Evolutionary
Software Systems Applications
and testing systems were self generating. *********************************************************** Evolutionary Systems and Artificial Life Evolutionary
Systems and Artificial Life by Luis
Rocha http://informatics.indiana.edu/rocha/alife.html#dds Luis Rocha's
CyberCorner http://informatics.indiana.edu/rocha/index.html Indiana
University School of Informatics http://informatics.indiana.edu/ Indiana
University-Purdue University Indianapolis School of Informatics http://www.informatics.iupui.edu/ Evolutionary
Systems Biology Group http://www.ai.sri.com/people_list/esb/ Polyworld: Using Evolution to Design Artificial Intelligence http://www.youtube.com/watch?v=_m97_kL4ox0&feature=related Evolving artificial creatures http://www.youtube.com/watch?v=d4BGLp0wcdE&feature=related ************************************************************************* Evolutionary
Linguistic Approach / Enfoque Lingüístico Evolutivo Linguistic Evolution / Evolución Lingüística Grammatical Evolution / Evolución Gramatical / Gramaticas Evolutivas ************************************************************************* Grammatical
Evolution: Evolving Programs for an Arbitrary Language Conor Ryan,
J. J. Collins, Michael O'Neill April 1998 Lecture
Notes In Computer Science; Vol. 1391 EuroGP '98: Proceedings of the First European Workshop on Genetic Programming Publisher: Springer-Verlag, UK (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=646806.706289&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 *********************************** Crossover in
Grammatical Evolution: A Smooth Operator? April 2000 Lecture
Notes In Computer Science; Vol. 1802 Proceedings of the European Conference
on Genetic Programming Publisher: Springer-Verlag, London, UK (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=646808.703948&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 *********************************** Crossover in
Grammatical Evolution Michael O'neill, Department of Computer Science & Information Systems, University
of Limerick, Ireland Conor Ryan,
Department of Computer Science & Information Systems, University
of Limerick, Ireland Maarten Keijzer, Free University, Amsterdam Mike Cattolico, Tiger Mountain Scientific, Inc. March 2003 Genetic Programming and Evolvable
Machines , Volume
4 Issue 1, March 2003 Publisher: Kluwer
Academic Publishers Hingham, MA, USA ABSTRACT “We present
an investigation into crossover in Grammatical Evolution that begins by
examining a biologically-inspired homologous crossover operator that is
compared to standard one and two-point operators. Results demonstrate that
this homologous operator is no better than the simpler one-point operator
traditionally adopted. An
analysis of the effectiveness of one-point crossover is then conducted by
determining the effects of this operator, by adopting a headless chicken-type
crossover that swaps randomly generated fragments in place of the evolved
strings. Experiments show detrimental effects with the utility of the
headless chicken operator. Finally,
the mechanism of crossover in GE is analysed and termed ripple crossover, due
to its defining characteristics. An experiment is described where ripple
crossover is applied to tree-based genetic programming, and the results show
that ripple crossover is more effective in exploring the search space of
possible programs than sub-tree crossover by examining the rate of premature
convergence during the run. Ripple crossover produces populations whose
fitness increases gradually over time, slower than, but to an eventual higher
level than that of sub-tree crossover.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=608284.608289&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 *********************************** http://portal.acm.org/citation.cfm?id=862025&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Book Publisher: Springer;
Hardcover, 1 edition (May 1, 2003), 160 pages Review From the reviews: "This is the first book
written on grammatical evolution, a new technique that is receiving
increasing attention and use. Therefore, the book fulfills an important role
… . The book contains a good description of grammatical evolution … .
‘Grammatical Evolution’ should be useful for specialists and Ph.D. students
in the field of grammatical evolution and genetic programming, and people
working in artificial intelligence and genetic algorithms in general. We
would advise it as a good resource for university libraries." (Manuel
Alfonseca and Alfonso Ortega, Genetic programming and Evolvable Machines,
Vol. 5, 2004) Product
Description “Grammatical Evolution: Evolutionary Automatic
Programming in an Arbitrary Language provides the first comprehensive
introduction to Grammatical Evolution, a novel approach to Genetic Programming
that adopts principles from molecular biology in a simple and useful manner,
coupled with the use of grammars to specify legal structures in a search.
Grammatical Evolution's rich modularity gives a unique flexibility, making it
possible to use alternative search strategies - whether evolutionary,
deterministic or some other approach - and to even radically change its
behavior by merely changing the grammar supplied. This approach to Genetic
Programming represents a powerful new weapon in the Machine Learning toolkit
that can be applied to a diverse set of problem domains. Beginning with an overview of the necessary
background material in Genetic Programming and Molecular Biology, Grammatical
Evolution: Evolutionary Automatic Programming in an Arbitrary Language
outlines the current state of the art in grammatical and
genotype-phenotype-based approaches. Following a description of Grammatical
Evolution and its application to a number of example problems, an in-depth
analysis of the approach is conducted, focusing on areas such as the
degenerate genetic code, wrapping, and crossover. The book continues with a
description of hot topics in Grammatical Evolution and presents possible
directions for future research.” (FGS Link, July, 2010)
http://www.amazon.com/Grammatical-Evolution-Evolutionary-Automatic-Programming/dp/1402074441 Book Review:
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary
Language Manuel
Alfonseca, Alfonso
Ortega Source Genetic
Programming and Evolvable Machines Volume
5 , Issue 4 (December 2004), Pages: 393 – 393, December 2004 Publisher: Kluwer Academic Publishers Hingham, MA, USA (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1011987.1011991&coll=ACM&dl=ACM&CFID=45851609&CFTOKEN=24619111 *********************************** Meta-grammar
constant creation with grammatical evolution by grammatical evolution Ian Dempsey, University
of Limerick, Limerick, Ireland Michael
O'Neill, University of Limerick, Limerick, Ireland Anthony
Brabazon, University College Dublin, Dublin, Ireland June 2005 GECCO '05: Proceedings of the 2005 conference on Genetic and evolutionary
computation, Washington DC, USA SESSION: Genetic programming Publisher: ACM ABSTRACT “This
study examines the utility of meta-grammar constant generation on a series of
benchmark problems. The performance of the meta-grammar approach is compared
to a grammar which incorporates grammatical ephemeral random constants, digit
concatenation, and an expression based approach. It is found that the
meta-grammar approach to constant creation is particularly beneficial on the
dynamic problem instances in terms of the best fitness values achieved.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1068009.1068289&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital Library
Members:
*********************************** Constant
creation in grammatical evolution Ian Dempsey,
Michael O'Neill, Anthony Brabazon Natural Computing Research and Applications Group,
University College Belfield, Dublin, Dublin, Ireland, April 2007 International Journal of Innovative
Computing and Applications , Volume
1 Issue 1, April
2007 Publisher: Inderscience Publishers, Geneva,
SWITZERLAND ABSTRACT “We present
an investigation into constant creation in Grammatical Evolution (GE), a form
of grammar-based Genetic Programming (GP). The methods for constant creation
evaluated include digit Concatenation (Cat) and a grammatical version of
ephemeral random constants called persistent random constants. Experiments
conducted on a diverse range of benchmark problems uncover clear advantages
for a digit Cat approach.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1359342.1359345&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 *********************************** Towards
models of user preferences in interactive musical evolution University of Limerick, Limerick, Ireland July 2007 GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary
computation, London, England Publisher: ACM ABSTRACT “We
describe the "bottom-up" construction of a system which aims to
build models of human musical preferences with strong predictive power. We use
Grammatical Evolution to construct models from toy datasets which mimic
real-world user-generated data. These models will ultimately substitute for
the subjective fitness functions that human users employ during Interactive
Evolution of melodies.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1276958.1277389&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
Library Members:
*********************************** Gecco 2007 Grammatical Evolution Tutorial Conor M. Ryan, University of Limerick, Limerick, Ireland July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary
computation, London, United Kingdom Publisher: ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1274000.1274126&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
Library Members:
*********************************** Gecco 2008
grammatical evolution tutorial R. Muhammad
Atif Azad, Conor Ryan University of Limerick, Limerick,
Ireland July 2008 GECCO '08: Proceedings of the 2008 GECCO conference companion on Genetic and
evolutionary computation, Atlanta, GA, USA Publisher: ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1388969.1389058&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
Library Members:
*********************************** GEVA:
grammatical evolution in Java Michael O'Neill, Erik Hemberg, Conor Gilligan, Eliott Bartley, James McDermott, Anthony Brabazon University College Dublin, Ireland, July 2008 SIGEVOlution , Volume 3 Issue 2, Summer 2008 Publisher: ACM ABSTRACT “We are delighted to announce the release of GEVA [1], an
open source software implementation of Grammatical Evolution (GE) in Java.
Grammatical Evolution in Java (GEVA) was developed at UCD's Natural Computing
Research & Applications group (http://ncra.ucd.ie).” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1527063.1527066&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
Library Members:
*********************************** Elevated
Pitch: Automated Grammatical Evolution of Short Compositions John Reddin, Trinity College Dublin, James
Mcdermott, University
College Dublin, Michael
O'Neill, University
College Dublin, April 2009 Lecture
Notes In Computer Science; Vol. 5484 EvoWorkshops '09: Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary
Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP,
EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG, Tübingen, Germany Section: EvoMUSA Publisher: Springer-Verlag Berlin, Heidelberg ABSTRACT “A system
for automatic composition using grammatical evolution is presented. Music is
created under the constraints of a generative grammar, and under the bias of an
automatic fitness function and evolutionary selection. This combination of
two methods is seen to be powerful and flexible. Human evaluation of
automatically-evolved pieces shows that a more sophisticated grammar in
combination with a naive fitness function gives better results than the
reverse.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1533570.1533643&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 ************************************************************************* Automatic
verilog code generation through grammatical evolution Ulya R. Karpuzcu, Istanbul Technical University, Maslak, Istanbul, Turkey, June
2005 GECCO '05: Proceedings of the 2005 workshops on Genetic and evolutionary
computation, SESSION:
UGWS Washington, D.C. Publisher: ACM ABSTRACT “This
work aims to investigate the automatic generation of Verilog code,
representing digital circuits through Grammatical Evolution (GE). Preliminary
tests using a simple full adder generation problem have been performed.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1102256.1102346&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
Library Members:
************************************************************************* 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:
Also published in: June 2000 SIGAPL APL Quote Quad Volume 30 Issue 4 *********************************** Automatic composition
of music by means of grammatical evolution Alfonso Ortega de la
Puente, Universidad
Autónoma de Madrid Rafael Sánchez Alfonso,
Universidad Autónoma de Madrid &
IBM Manuel Alfonseca
Moreno, 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:
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:
************************************************************************* Grammex:
defining grammars by example
Henry
Lieberman, Media Laboratory Massachusetts Institute of
Technology Cambridge, MA Bonnie A.
Nardi, Apple Computer, Advanced Technology Group, 1
Infinite Loop, Cupertino, CA David Wright, Apple
Computer, Advanced Technology Group, 1 Infinite Loop, Cupertino, CA April 1998 Conference
on Human Factors in Computing Systems CHI 98
conference summary on Human factors in computing systems Los Angeles, California, United States, Pages: 11 –
12, Year of Publication: 1998 Publisher: ACM ABSTRACT “Parsers
are powerful tools for computer understanding of text, whether the language
is a natural language or a formal language. To make the computational power
of these tools fully available to an end user, a parser should be
user-extensible. Until now, a user who wished to control a parser was forced
to write or edit a grammar, a text file containing rules. Editing grammars is
often difficult and error-prone for end users since the effect of writing
specific rules, and interaction between rules, can often be unclear. Grammex
[“Grammars by Example”] is the first direct manipulation interface designed
to allow ordinary users to define grammars interactively. Instead of writing
a grammar in an abstract rule language, the user presents concrete examples
of text that he or she would like the parser to recognize. The user describes
the text by selecting substrings, and choosing possible interpretations of
the text from popup menus of suggestions heuristically computed by Grammex.
Grammex compiles grammar rules that can be used as the input to a traditional
parser.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=286498.286504&coll=ACM&dl=ACM&CFID=46776952&CFTOKEN=58818603 Full text available for ACM Digital
Library Members:
Grammex:
Defining Grammars by Example Henry
Lieberman Media
Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA (FGS Link, July, 2010) http://web.media.mit.edu/~lieber/Lieberary/Grammex/Grammex-Intro.html ******************************** Training
agents to recognize text by example Henry
Lieberman, Massachusetts
Institute of Technology, Cambridge Bonnie A.
Nardi, AT&T
Labs West, Menlo Park, CA David Wright, Apple Computer, Cupertino, CA April 1999 International
Conference on Autonomous Agents AGENTS '99: Proceedings of the third annual conference on Autonomous Agents Seattle, Washington, United States,
Pages: 116 – 122, Year of Publication: 1999 Publisher: ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=301136.301176&coll=ACM&dl=ACM&CFID=46776952&CFTOKEN=58818603 Full text available for ACM Digital
Library Members:
Training
Agents to Recognize Text by Example Henry
Lieberman Media
Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA David
Wright, Apple Computer, 1 Infinite Loop, Cupertino, CA 95014 USA “An
important function of an agent is to be "on the lookout" for bits
of information that are interesting to its user, even if these items appear
in the midst of a larger body of unstructured information. But how to tell
these agents which patterns are meaningful and what to do with the result? Especially
when agents are used to recognize text, they are usually driven by parsers
which require input in the form of textual grammar rules. Editing grammars is
difficult and error-prone for end users. Grammex ["Grammars by
Example"] is the first direct manipulation interface designed to allow
non-expert users to define grammars interactively. The user presents concrete
examples of text that he or she would like the agent to recognize. Rules are
constructed by an iterative process, where Grammex heuristically parses the
example, displays a set of hypotheses, and the user critiques the system’s
suggestions. Actions to take upon recognition are also demonstrated by
example.” (FGS Link, July, 2010) http://web.media.mit.edu/~lieber/Lieberary/Grammex/Grammex.html ************************************************************************* Can a parser
be generated from examples? Marjan Mernik, University of Maribor, Smetanova 17, 2000 Maribor,
Slovenia Goran
Gerlič, University
of Maribor, Smetanova 17, 2000 Maribor, Slovenia Viljem Žumer, University of Maribor, Smetanova 17, 2000 Maribor,
Slovenia Barrett R.
Bryant, The
University of Alabama at Birmingham, Birmingham, AL March 2003 Symposium
on Applied Computing SAC
'03: Proceedings
of the 2003 ACM symposium on Applied computing Melbourne, Florida SESSION: Programming languages and
object technologies, Pages: 1063 - 1067 Publisher: ACM ABSTRACT “One of
the open problems in the area of domain-specific languages is how to make
domain-specific language development easier for domain experts not versed in
a programming language design. Possible approaches are to build a
domain-specific language from parameterized building blocks or by language
(grammar) induction. This paper uses an evolutionary approach to grammar
induction. Grammar-specific genetic operators for crossover and mutation are
proposed to achieve this task. Suitability of the approach is shown by small
experiments where underlying grammars are successfully genetically obtained
and parsers are than automatically generated.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=952532.952740&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
*********************************** Context-free
grammar induction using genetic programming F. Javed, University of Alabama at Birmingham, Birmingham, AL B. R. Bryant, University of Alabama at Birmingham, Birmingham, AL M.
Črepinšek, University
of Maribor, Maribor, Slovenia M. Mernik, University of Maribor, Maribor, Slovenia A. Sprague, University of Alabama at Birmingham, Birmingham, AL April 2004 ACM-SE 42: Proceedings of the 42nd annual Southeast regional conference Huntsville,
Alabama Publisher: ACM ABSTRACT “While
grammar inference is used in areas like natural language acquisition,
syntactic pattern recognition, etc., its application to the programming
language problem domain has been limited. We propose a new application area
for grammar induction which intends to make domain-specific language
development easier and finds a second application in renovation tools for
legacy systems. The genetic programming approach is used for grammatical
inference. Our earlier work used grammar-specific heuristic operators in
tandem with non-random construction of the initial grammar population and
succeeded in inducing small grammars.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=986537.986635&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
*********************************** Extracting
grammar from programs: brute force approach Matej Črepinšek, Marjan Mernik, Viljem Žumer University of Maribor, Maribor,
Slovenia, SIGPLAN Notices , Volume 40 Issue 4, April 2005 Publisher: ACM ABSTRACT Extracting
grammar from programs attracts researchers from several fields such as
software engineering, pattern recognition, computational linguistic and
natural language acquisition. So far, only regular grammar induction has been
successful, while purposeful context-free grammar induction is still elusive.
We discuss the search space of context-free grammar induction and propose the
Brute Force approach to the problem which, along with its various
enhancements, can be further used in collaboration with the Evolutionary
approach, as described in the accompanying paper. (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1064165.1064171&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
*********************************** Extracting
grammar from programs: evolutionary approach Matej Črepinšek, University of Maribor, Maribor, Slovenia Marjan Mernik, University of Maribor, Maribor, Slovenia Faizan Javed,
The University of Alabama at Birmingham, Birmingham, AL Barrett R. Bryant, The University of Alabama at Birmingham, Birmingham, AL Alan Sprague,
The University of Alabama at Birmingham, Birmingham, AL SIGPLAN Notices , Volume 40 Issue 4, April 2005 Publisher: ACM ABSTRACT “The
paper discusses context-free grammar (CFG) inference using
genetic-programming with application to inducing grammars from programs
written in simple domain-specific languages. Grammar-specific heuristic
operators and non-random construction of the initial population are proposed
to achieve this task. Suitability of the approach is shown by small examples
where the underlying CFG's are successfully inferred.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1064165.1064172&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
Library Members:
*********************************** An
unsupervised incremental learning algorithm for domain-specific language
development
Faizan Javed, Department of Computer & Information Sciences,
University of Alabama at Birmingham, Birmingham, Alabama, USA Marjan Mernik, Faculty of Electrical Engineering and Computer
Science, University of Maribor, Maribor, Slovenia Barrett R.
Bryant, Department
of Computer & Information Sciences, University of Alabama at Birmingham,
Birmingham, Alabama, USA Alan Sprague, Department of Computer & Information Sciences,
University of Alabama at Birmingham, Birmingham, Alabama, USA August 2008 Applied Artificial Intelligence , Volume 22 Issue 7-8, August 2008 Publisher: Taylor & Francis, Inc. Bristol, PA, USA ABSTRACT “While
grammar inference (or grammar induction) has found extensive application in
the areas of robotics, computational biology, and speech recognition, its
application to problems in programming language and software engineering
domains has been limited. We have found a new application area for grammar
inference which intends to make domain-specific language development easier for
domain experts not well versed in programming language design, and finds a
second application in construction of renovation tools for legacy software
systems. As a continuation of our previous efforts to infer context-free
grammars (CFGs) for domain-specific languages which previously involved a
genetic-programming based CFG inference system, we discuss extensions to the
inference capabilities of GenInc, an incremental learning algorithm for
inferring CFGs. We show that these extensions enable GenInc to infer more
comprehensive grammars, discuss the results of applying GenInc to various
domain-specific languages and evaluate the results using a comprehensive
suite of grammar metrics.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1451363.1451367&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 ************************************************************************* Evolutionary
music composer integrating formal grammar Yaser M. A. Khalifa, Badar K. Khan, Jasmin Begovic, Airrion Wisdom, Andrew Maxymillian Wheeler State
University of New York, New Paltz, NY July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation, London, United Kingdom Publisher: ACM ABSTRACT “In this
paper, an autonomous music composition tool is developed using Genetic
Algorithms. The production is enhanced by integrating formal grammar rules. A
formal grammar is a collection of either or both descriptive or prescriptive
rules for analyzing or generating sequences of symbols. In music, these
symbols are musical parameters such as notes and their attributes. The
composition is conducted in two Stages. The first Stage generates and
identifies musically sound patterns (motifs). In the second Stage, methods to
combine different generated motifs and their transpositions are applied.
These combinations are evaluated and as a result, musically fit phrases are
generated. Four musical phrases are generated at the end of each program run.
The generated music pieces will be translated into Guido Music Notation (GMN)
and have alternate representation in Musical Instrument Digital Interface
(MIDI). The Autonomous Evolutionary Music Composer (AEMC) was able to create interesting
pieces of music that were both innovative and musically sound.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1274000.1274020&coll=ACM&dl=ACM&CFID=47166338&CFTOKEN=60514033 Full text available for ACM Digital
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************************************************************************* Paul Viola,
Microsoft Research, Redmond, WA Mukund Narasimhan, University of Washington, Seattle, WA August 2005 SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on
Research and development in information retrieval Publisher: ACM (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1076034.1076091&coll=ACM&dl=ACM&CFID=46542654&CFTOKEN=84397846 Full text available for ACM Digital
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Learning to Extract Information from Semistructured
Text using a Discriminative Context Free Grammar Draft submitted to the conference SIGIR 2005 Paul
Viola, Mukund Narasimhand ABSTRACT “In recent work, conditional Markov chain models
(CMM) have been used to extract information from semi-structured text (one
example is the Conditional Random Field [10]). Applications range from finding the author and title
in research papers to finding the phone number and street address in a web
page. The CMM framework combines a priori knowledge encoded as features with
a set of labeled training data to learn an efficient extraction process. We
will show that similar problems can be solved more e_ectively by learning a
discriminative context free grammar from training data. The grammar has
several distinct advantages: long range, even global, constraints can be used
to disambiguate entity labels; training data is used more efficiently; and a
set of new more powerful features can be introduced. The specific problem we
consider is of extracting personal contact, or address, information from
unstructured sources such as documents and emails. While linear-chain CMMs
perform reasonably well on this task, we show that a statistical parsing
approach results in a 50% reduction in error rate. This system also has the
advantage of being interactive, similar to the system described in [9]. In
cases where there are multiple errors, a single user correction can be
propagated to correct multiple errors automatically. Using a discriminatively
trained grammar, 93.71% of all tokens are labeled correctly (compared to
88.43% for a CMM) and 72.87% of records have all tokens labeled correctly
(compared to 45.29% for the CMM).” (FGS Link, July, 2009) http://research.microsoft.com/en-us/um/people/viola/pubs/docextract/contact_sigir05.pdf ************************************************************************* Symbolic
regression using abstract expression grammars Freeman Investment Management, Henderson, NV, USA, June 2009 GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary
Computation, Shanghai, China Publisher: ACM ABSTRACT “Abstract
Expression Grammars have the potential to integrate Genetic Algorithms,
Genetic Programming, Swarm Intelligence, and Differential Evolution into a
seamlessly unified array of tools for use in symbolic regression. The
features of abstract expression grammars are explored, examples of
implementations are provided, and the beneficial effects of abstract
expression grammars are tested with several published nonlinear regression
problems.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1543834.1543960&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
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************************************************************************* Grammatical
Swarm: The generation of programs by social programming Michael O'Neill, Anthony Brabazon Natural Computing Research & Applications Group,
University College Dublin, Dublin, Ireland, November 2006 Natural Computing: an international
journal , Volume
5 Issue 4, November 2006 Publisher: Kluwer Academic Publishers ABSTRACT “This
study examines Social Programming, that is, the construction of programs
using a Social Swarm algorithm based on Particle Swarm Optimization. Each
individual particle represents choices of program construction rules, where
these rules are specified using a Backus---Naur Form grammar. This study
represents the first instance of a Particle Swarm Algorithm being used to
generate programs. A selection of benchmark problems from the field of
Genetic Programming are tackled and performance is compared to Grammatical
Evolution. The results demonstrate that it is possible to successfully
generate programs using the Grammatical Swarm technique. An analysis of the
Grammatical Swarm approach is presented on the dynamics of the search. It is
found that restricting the search to the generation of complete programs, or
with the use of a ratchet constraint forcing individuals to move only if a
fitness improvement has been found, can have detrimental consequences for the
swarms performance and dynamics.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1180219.1180223&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 ************************************************************************* Evolutionary
swarm design of architectural idea models Sebastian von Mammen, Christian Jacob University of Calgary, Calgary, AB, Canada, July
2008 GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary
computation, Atlanta, GA, USA Publisher: ACM ABSTRACT “In this
paper we present a swarm grammar system that makes use of bio-inspired
mechanisms of reproduction, communication and construction in order to build
three-dimensional structures. Ultimately, the created structures serve as
idea models that lend themselves to inspirations for architectural designs. Appealing
design requires structural complexity. In order to computationally evolve
swarm grammar configurations that yield interesting architectural models, we
observe their productivity, coordination, efficiency, and their unfolding
diversity during the simulations. In particular, we measure the numbers of
collaborators in each swarm individual's neighborhood, and we count the types
of expressed swarm agents and built construction elements. At the end of the
simulation the computation time is saved and the created structures are rated
with respect to their approximation of pre-defined shapes. These ratings are
incorporated into the fitness function of a genetic algorithm. We show that
the conducted measurements are useful to direct an evolutionary search
towards interesting yet well-constrained architectural idea models.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1389095.1389115&coll=ACM&dl=ACM&CFID=47460721&CFTOKEN=36888833 Full text available for ACM Digital
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************************************************************************* Generative
and developmental systems Kenneth O. Stanleym University
of Central Florida, Orlando, FL, USA, July 2008 GECCO '08: Proceedings of the 2008 GECCO conference companion on Genetic and
evolutionary computation Publisher: ACM (FGS Link, July, 2009) Full text available for ACM Digital
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************************************************************************* From
Watson-Crick L systems to Darwinian P systems Erzsébet Csuhaj-Varjú, Computer and Automation Research Institute, Hungarian Academy of
Sciences, Kende u. 13-17, 1111 Budapest, Hungary Carlos Martín-Vide, Research Group on Mathematical Linguistics, Rovira i Virgili
University, Pl. Imperial Tárraco 1, 43005 Tarragona, Spain Gheorgh Păaun, Research Group on Mathematical Linguistics, Rovira i Virgili
University, Pl. Imperial Tárraco 1, 43005 Tarragona, Spain;
Institute of Mathematics of the Romanian Academy, P.O. Box 1-764, 70700
Bucureşti, Romania Arto Salomaa, Turku Centre
for Computer Science, Lemminkäisenkatu 14A, 20520 Turku, Finland August 2003 Natural Computing: an international
journal , Volume
2 Issue 3, 2003 Publisher: Kluwer
Academic Publishers Hingham, MA, USA ABSTRACT “Watson-Crick
L systems are language generating devices making use of Watson-Crick
complementarity, a fundamental concept of DNA computing. These devices are
Lindenmayer systems enriched with a trigger for complementarity transition:
if a ``bad'' string is obtained, then the derivation continues with its
complement which is always a ``good'' string. Membrane systems or P systems
are distributed parallel computing models which were abstracted from the
structure and the way of functioning of living cells. In this paper, we first
interpret the results known about the computational completeness of
Watson-Crick E0L systems in terms of membrane systems, then we introduce a
related way of controlling the evolution in P systems, by using the triggers
not in the operational manner (i.e., turning to the complement in a ``bad''
configuration), but in a ``Darwinian'' sense: if a ``bad'' configuration is
reached, then the system ``dies'', that is, no result is obtained. The
triggers (actually, the checkers) are given as finite state multiset
automata. We investigate the computational power of these P systems. Their
computational completeness is proved, even for systems with non-cooperative
rules, working in the non-synchronized way, and controlled by only two finite
state checkers; if the systems work in the synchronized mode, then one
checker for each system suffices to obtain the computational completeness.” (FGS Link, July, 2009) *********************************** A remark on
evolutionary systems Erzsébet Csuhaj-Varjú, Computer and Automation Research Institute, Hungarian Academy of
Sciences, Kende utca 13-17, H-1111 Budapest, Hungary Jürgen Dassow, 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) ************************************************************************* Regular
expression generation through grammatical evolution Ahmet
Cetinkaya,
Istanbul Technical University July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation, London, United Kingdom Publisher: ACM ABSTRACT “This
study investigates automatic regular expression generation using Grammatical
Evolution. The software implementation is based on a subset of POSIX regular
expression rules. For fitness calculation, a multiline text file is supplied.
Lines which are required to match with generated regular expressions are
specified beforehand. Fitness is evaluated according to the successful match
results. Using this fitness evaluation strategy, preliminary tests have been
performed on different files. Results indicate that the Grammatical Evolution
approach to automatic generation of regular expressions is promising.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1274000.1274089&coll=ACM&dl=ACM&CFID=47333874&CFTOKEN=32485152 Full text available for ACM Digital
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************************************************************************* Active
Coevolutionary Learning of Deterministic Finite Automata December 2005 The Journal of Machine Learning Research , Volume 6 Publisher: MIT Press ABSTRACT “This
paper describes an active learning approach to the problem of grammatical
inference, specifically the inference of deterministic finite automata
(DFAs). We refer to the algorithm as the estimation-exploration algorithm
(EEA). This approach differs from previous passive and active learning
approaches to grammatical inference in that training data is actively
proposed by the algorithm, rather than passively receiving training data from
some external teacher. Here we show that this algorithm outperforms one
version of the most powerful set of algorithms for grammatical inference,
evidence driven state merging (EDSM), on randomly-generated DFAs. The
performance increase is due to the fact that the EDSM algorithm only works
well for DFAs with specific balances (percentage of positive labelings),
while the EEA is more consistent over a wider range of balances. Based on
this finding we propose a more general method for generating DFAs to be used
in the development of future grammatical inference algorithms.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1046920.1194900&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
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************************************************************************* A more
bio-plausible approach to the evolutionary inference of finite state machines Hooman Shayani, Peter J. Bentley July 2007 GECCO '07: Proceedings of the 2007 GECCO conference companion on Genetic and
evolutionary computation, University College London Publisher: ACM ABSTRACT “With
resemblance of finite-state machines to some biological mechanisms in cells
and numerous applications of finite automata in different fields, this paper
uses analogies and metaphors to introduce an element of bio-plausibility to
evolutionary grammatical inference. Inference of a finite-state machine that
generalizes well over unseen input-output examples is an NP-complete problem.
Heuristic algorithms exist to minimize the size of an FSM keeping it
consistent with all the input-output sequences. However, their performance
dramatically degrades in presence of noise in the training set. Evolutionary
algorithms perform better for noisy data sets but they do not scale well and
their performance drops as size or complexity of the target machine grows.
Here, inspired by a biological perspective, an evolutionary algorithm with a
novel representation and a new fitness function for inference of Moore
finite-state machines of limited size is proposed and compared with one of
the latest evolutionary techniques.” (FGS Link, July, 2009) http://portal.acm.org/citation.cfm?id=1274000.1274039&coll=ACM&dl=ACM&CFID=46014229&CFTOKEN=26545706 Full text available for ACM Digital
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************************************************************************* Evolved
finite state controller for hybrid system Jean-François
Dupuis, Technical
University of Denmark, Kgs. Lyngby, Denmark Zhun Fan, Technical University of Denmark, Kgs. Lyngby,
Denmark Erik Goodman, Michigan State University, East Lansing, MI, USA June 2009 GEC '09: Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary
Computation Publisher: ACM ABSTRACT “This
paper presents an evolutionary methodology to automatically generate finite
state automata (FSA) controllers to control hybrid systems. FSA controllers
for a case study of two-tank system have been successfully obtained using the
proposed evolutionary approach. Experimental results show that these
controllers have good performance on the set of training targets as well as
on a randomly generated set of validation targets.” (FGS Link, July, 2009) Full text available for ACM Digital
Library Members:
*********************************************************** Computación Evolutiva (CE) ********************************************* Dr. Carlos A. Coello Coello Introducción a la Computación Evolutiva (Presentación) Dr. Carlos A. Coello Coello http://delta.cs.cinvestav.mx/~ccoello/compevol/clase2-2006.pdf Introducción a la Computación Evolutiva (Notas de Curso) Dr. Carlos A. Coello Coello http://weblidi.info.unlp.edu.ar/catedras/neuronales/Apunte%20Coello%20Coello.pdf Introducción a la Computación Evolutiva Carlos A. Coello Coello http://www.cs.cinvestav.mx/~EVOCINV/tutorials/computacionevolutiva..htm The
Evolutionary Computation Group at CINVESTAV-IPN (EVOCINV) http://www.cs.cinvestav.mx/~EVOCINV/ http://www.cs.cinvestav.mx/~EVOCINV/publications.html GECCO
Genetic and Evolutionary Computation Conference http://gal4.ge.uiuc.edu:8080/GECCO-2003/ ******************************************** Genetic programming / Programación genética Algoritmos genéticos The
Genetic Algorithms Archive is a repository for information related to
research in genetic algorithms and other forms of evolutionary computation http://www.aic.nrl.navy.mil/galist/ Home Page
of John R. Koza http://www.genetic-programming.com/johnkoza.html John
Koza’s Publications on Genetic Programming http://www.genetic-programming.com/#_John_Koza’s_Publications Welcome
to www.genetic-programming.com (the home
page of Genetic Programming Inc., a privately funded research group that does
research in applying genetic programming) http://www.genetic-programming.com/ ************************** Algoritmos genéticos (general) http://www.rennard.org/alife/english/gavintrgb.html http://cs.felk.cvut.cz/~xobitko/ga/ (aplicados a juegos) http://www.gignews.com/gregjames1.htm Evolutionary
algorithms scholarpedia http://www.scholarpedia.org/article/Evolutionary_algorithms Informática evolutiva Juan Julián Merelo Guervós http://geneura.ugr.es/~jmerelo/ie/intro.htm Informática evolutiva: Algoritmos genéticos Juan Julián Merelo Guervós http://geneura.ugr.es/~jmerelo/ie/ags.htm ******************************************** Algoritmos Culturales Alberto Ochoa O. Zezzatti, Gaceta Ide@s CONCYTEG Año 3. Núm. 31, 21 de enero de 2008 http://energia.guanajuato.gob.mx/gaceta/Gacetaideas/Archivos/31052008_ALGORITMOS_CULTURALES.pdf ********************************************* Angel García Baños Curso de Computación Evolutiva (CE) Angel García Baños Profesor titular de la Escuela de Ingeniería de Sistemas y Computación de la Universidad del Valle (Cali - Colombia) Las transparencias que componen el curso Tema 1: Introducción
a la asignatura. Introducción a la evolución. Tema 2 : Algoritmos
genéticos. Paralelismo. Teorema fundamental. Multiobjetivo. Ejemplos con
problemas combinatorios. Tema 2 : Algoritmos genéticos. Paralelismo. Teorema fundamental. Multiobjetivo. Ejemplos con problemas combinatorios. Tema 3: Programación
genética. Evolución gramatical. Programación por expresión genética. Tema 4 :
Programación evolutiva. Estrategias evolutivas. Tema 5 : Sistemas
clasificadores. Tema 6: Simulated annealing. Tema 7: Límites
computacionales fundamentales. Tema 8:
Autoduplicación. Complejidad. Autómatas celulares. Constructor universal de
von Neumann. Tema 9: Teoría de
juegos. Dilema del prisionero. Emergencia. Señalización. Tema 10: Mundos
artificiales. Hormigas Artificiales. Arte evolutivo por computador. Tema 11: Caos.
Fractales. Tema 12: Teorema de
"no-free-lunch". Tema 13:
Conclusiones y resumen. http://eisc.univalle.edu.co/~angarcia/ce/ce_material.html Mundos Artificiales Angel García Baños Grupo EVA (Evolución y Vida Artificiales), Escuela de Ingeniería de Sistemas y Computación, Facultad de Ingenierías, Universidad del Valle COLOQUIO EPISTEME, marzo de 2008 v2 http://eisc.univalle.edu.co/~eva/mundosArtificiales.pdf Computación Evolutiva (CE) Teorema de No-Free-Lunch Angel García Baños Escuela de Ingeniería de Sistemas y Computación, Universidad del Valle, 04 de febrero de 2008 http://eisc.univalle.edu.co/~angarcia/ce/ce_clases/ce-12_%20NFL.pdf ***********************************************************
Evolving Matrices
/ Matrices Evolutivas Adquisición de conocimientos utilizando matriz evolutiva Jorge Martínez Muñoz Carreras : Ing. Industrial [Maestria], Tesis Plantel : Instituto
Tecnológico de Toluca http://www.triangulum.org.mx/CO_FichaLibro.asp?IDI=9993&DSD=LT Learning
Matrices and Their Applications Institute for Information Processing and
Communication, Technische Hochschule Karlsruhe; http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4038031 “Fast Diagonalization of
Evolving Matrices: Application to Spin-Fermion Models” E. F. D’Azevedo*, P. K. Nukala, G. Alvarez Oak Ridge National Laboratory Advanced
Scientific Computing Research Applied Mathematics http://www.sc.doe.gov/ascr/Research/AM/07AccompPDFs/ORNL-DAzevedo.pdf EPSRC Consortium: Multi-Scale Diffusion Phenomena for Advanced Materials
Manufacture Draft Proposal 24/09/07 “evolving
matrices are ones in which the matrix microstructure
changes with time. Interacting matrices are ones in which there is strong” http://www.umi.surrey.ac.uk/UserFiles/file/MSDP%20Draft%20070924.pdf The Markov Process as a Compositional Model: A Survey and Tutorial Charles Ames Leonardo, Vol. 22, No. 2 (1989), pp. 175-187 Published by: The MIT Press “If standard first- order
matrices will not suffice, one might consider Nth-order matrices, evolving
matrices or chains of chains. ...” http://www.jstor.org/pss/1575226 Interior Penalty Finite Element Approximation of Navier-Stokes Equations
and Application to Free Surface Flows Christoph Winkelmann Public Defense CMCS – EPFL, December 5th 2007 http://iacs.epfl.ch/~winkelma/docs/public_defense.pdf *********************************************************** Evolvable hardware /
Hardware evolutivo Evolvable
hardware “Evolvable hardware (EH) is a new field about the use of evolutionary algorithms (EA) to create specialized electronics without manual engineering. It brings together reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment.” (Wikipedia, 8/vii/2010) http://en.wikipedia.org/wiki/Evolvable_hardware On evolvable
hardware Timothy GW
Gordon and Peter J. Bentley University College London http://www.cs.ucl.ac.uk/staff/t.gordon/scie.pdf Crean el primer prototipo de hardware evolutivo Abre nuevas vías para el desarrollo de una Inteligencia Artificial más avanzada Raúl Morales, TENDENCIAS21, TENDENCIAS INFORMÁTICAS, Martes 3 Abril 2007 “Un equipo de investigadores noruegos ha creado la primera pieza de un hardware que usa la evolución para cambiar su diseño y adaptarse a la función que tiene que desempeñar. Este hardware evoluciona como lo harían las especies animales o vegetales y se adapta al medio sin que sufra su funcionalidad y en apenas unos segundos. En vez de actualizar software, el hardware es capaz de evolucionar, probar soluciones ante los problemas y adoptar la decisión más adecuada. Esta investigación abre la puerta a desarrollos de Inteligencia Artificial más avanzados. Los profesores Kyrre Glette y Jim Torresen de la Facultad de Informática de la Universidad de Oslo han publicado un artículo en el que presentan un prototipo de hardware evolutivo capaz de adaptarse en unos segundos a la función que desarrolla en cada momento. La Universidad de Oslo ha explicado este proyecto en su revista Apollon, del que se ha hecho eco Bitsofnews.” http://www.tendencias21.net/Crean-el-primer-prototipo-de-hardware-evolutivo_a1484.html A semejanza de las neuronas Producen el primer circuito de procesamiento evolutivo MADRID, 26 Abr. (EUROPA PRESS) -, 20100426 “ Los circuitos de procesamiento de
información en los ordenadores de la era digital son estáticos. Como ventaja
comparativa, en nuestro cerebro, estos mismos circuitos de procesamiento de
información --las neuronas-- evolucionan continuamente para resolver
problemas complejos. Ahora, un equipo internacional de investigadores del National Institute of Information and Comunication Technology, de Japón, y la Universidad Tecnológica de Michigan ha creado un proceso similar de evolución en el circuito de una base orgánica molecular que puede resolver problemas complejos. Esta es la primera vez que se produce un circuito evolutivo a semejanza de los que forman el cerebro.” *********************************************************** Evolving Cellular Automata Evolving
Cellular Automata: Papers Last updated:
17 Feb 00 http://cse.ucdavis.edu/~evca/evabstracts.html *********************************************************** Evolution and the Theory of
Games
Evolution and
the Theory of Games University of
Sussex Cambridge University Press, 1982 “Professor John Maynard Smith has written an account of a new way of
thinking about evolution which has been developed in the last ten years. The
theory of games, first developed to analyse economic behaviour, is modified
so that it can be applied to evolving populations. John Maynard Smith's
concept of an evolutionarily stable strategy is relevant whenever the best
thing for an animal or plant to do depends on what others are doing. The
theory leads to testable predictions about the evolution of behaviour, of sex
and genetic systems, and of growth and life history patterns. This book
contains the first full account of the theory, and of the data relevant to
it. The account is aimed at senior undergraduate and graduate students, teachers
and research workers in animal behaviour, population genetics and
evolutionary biology. The book will also be of interest to mathematicians and
game theorists; the mathematics has been largely confined to appendixes so
that the main text may be easily followed by biologists.” (FGS Link,
26/vii/2010) http://www.cambridge.org/catalogue/catalogue.asp?isbn=0521288843 John Maynard
Smith “John
Maynard Smith,[1] F.R.S. (6 January 1920 – 19 April
2004) was a British theoretical evolutionary biologist and geneticist.
Originally an aeronautical engineer during the Second
World War, he then took a second degree in genetics under the well-known
biologist J.B.S. Haldane. Maynard Smith was instrumental in
the application of game theory to evolution and theorized on other
problems such as the evolution of sex and signalling theory. … In 1973
Maynard Smith formalised a central concept in game
theory called the evolutionarily stable strategy
(ESS), based on a verbal argument by George
R. Price. This area of research culminated in his 1982 book Evolution and the Theory of Games”
(Wikipedia 26/vii/2010) http://en.wikipedia.org/wiki/John_Maynard_Smith#Evolution_and_the_Theory_of_Games John Maynard Smith “John Maynard Smith (6 de enero de 1920 – 19 de abril de 2004). Genetista e investigador en biología evolutiva. ...Maynard Smith es una de las figuras clave de la escuela neodarwinista. Originalmente ingeniero aeronáutico durante la segunda guerra mundial, cambiando sorpresivamente su visión al campo de la genética y la ecología bajo el mando del famoso biólogo J. B. S. Haldane cuando cambio por completo su formación académica y se vinculó al University College London. los primeros aportes de Maynard Smith a la biología evolutiva fue simplemente reconocer de forma inmediata la idea de otro biólogo evolutivo W. D. Hamilton el cual planteaba que en humanos y en insectos eusociales opera sobre los genes y no sobre la población como otros proponían, por primera vez se utilizo el termino " Kin selection " Por ejemplo, fue a partir de sus ideas como Richard Dawkins desarrolló la teoría del gen egoísta. Maynard Smith Fue el primero en aplicar la teoría de juegos al estudio de la biología evolutiva.” (Wikipedia 26/vii/2010) http://es.wikipedia.org/wiki/John_Maynard_Smith Evolutionary
game theory “Evolutionary
game theory (EGT) is the application of game
theory to interaction dependent strategy evolution in populations. EGT is
useful in a biological context by defining a framework of strategies in which
adaptive features can be modeled. It originated in 1973 with John Maynard Smith and George
R. Price's formalization of evolutionarily stable strategies
as an application of the mathematical theory of games to biological contexts[1], arising from
the realization that frequency dependent fitness introduces a strategic
aspect to evolution. EGT differs from classical game theory by focusing on
the dynamics of strategy change more than the properties of strategy equilibria. Despite its name, evolutionary game
theory has become of increasing interest to economists,
sociologists,
anthropologists,
and philosophers.”
(Wikipedia 25/vii/2010) http://en.wikipedia.org/wiki/Evolutionary_game_theory Teoría evolutiva de juegos “La teoría evolutiva de juegos (EGT) es la aplicación de modelos inspirados en genética de la población de cambios de la frecuencia genética en poblaciones a la teoría de juegos. Difiere de la teoría de juegos clásica en que se concentra en las dinámicas de la estrategia en lugar de sus equilibrios. A pesar de su nombre, la teoría evolutiva de juegos se aplica más en economía que en biología. La metodología habitual para estudiar las dinámicas evolutivas en un juego es a través de las ecuaciones de replicador. Las ecuaciones de replicador asumen población infinita, tiempo continuo y mezcla completa. Los atractores de las ecuaciones son equivalentes con los estados evolutivamente estables.” (Wikipedia 25/vii/2010) http://es.wikipedia.org/wiki/Teor%C3%ADa_evolutiva_de_juegos Evolutionary
game theory: Temporal and spatial effects beyond replicator dynamics Carlos P. Roca, José A. Cuesta, Angel Sánchez Available
online 7 August 2009 “Abstract Evolutionary
game dynamics is one of the most fruitful frameworks for studying evolution
in different disciplines, from Biology to
Economics. Within this context, the approach of choice for many researchers
is the so-called replicator equation, that describes mathematically
the idea that those individuals performing better have more offspring and
thus their frequency in the population grows.
While very many interesting results have been obtained with this equation in
the three decades elapsed since it was first proposed,
it is important to realize the limits of its applicability. One particularly
relevant issue in this respect is that of non-meanfield effects,
that may arise from temporal fluctuations or from spatial correlations, both
neglected in the replicator equation. This review
discusses these temporal and spatial effects focusing on the non-trivial
modifications they induce when compared to the outcome
of replicator dynamics. Alongside this question, the hypothesis of linearity
and its relation to the choice of the rule for strategy
update is also analyzed. The
discussion is presented in terms of the emergence of cooperation, as one of
the current key problems in
Biology and in other disciplines.” http://gisc.uc3m.es/~cuesta/PDFs/PLR06208.pdf *********************************************************** |
