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Topic: Evolutionary Computation

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	Criticisms of Evolutionary Computation - Draft paper
		All evolutionary computation is premised upon a "genetic" representation of either a candidate solution for an optimization problem (as in GAs) or an instantiation of a program to be "run" on a virtual or real machine (AL and GP).
		Computer simulation, once held to be either a potential indicator of merit or an actual falsifier of natural selection, is then treated as essentially irrelevant to natural selection.
		It is certainly true that computer simulations are less complex than biological problems, but the claim at issue is not that EC captures all the nuances of biology, but rather that EC gives a demonstration of the adaptive capabilities of natural selection as an algorithm.
	www.antievolution.org /people/wre/essays/antiec.html (2534 words)

	IEEE Transactions on Evolutionary Computation Editorial (Site not responding. Last check: 2007-10-13)
		The scope is intentionally broad; although the primary focus is on the practical application of evolutionary algorithms, a rigorous theoretical understanding of the algorithms that are employed must also be given suitable attention.
		Fogel has published over 100 papers on evolutionary computation in journals and conferences, and is the author of two books, most recently Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, IEEE Press, 1995.
		Fogel was the Founding President of the Evolutionary Programming Society (1991) and has served on numerous program committees of conferences in the area of evolutionary computation.
	www.ewh.ieee.org /tc/nnc/pubs/tec/ec_editorial.html (1009 words)

	ATI's Neural and Evolutionary Computation course
		He is the author of over 125 papers in journal and conferences, the author and editor of two books on evolutionary computation "Evolutionary Computation: Toward a New Philosophy of Machine Intelligence," and "Evolutionary Computation: The Fossil Record," He is the founding Editor-in-Chief of the IEEE Transactions on Evolutionary Computation.
		Porto is an Associate Editor for the IEEE Transactions on Evolutionary Computation, and was the General Chairman of the 7th Annual Conference on Evolutionary Programming.
		How evolutionary computation can be used to assist in the training and design of neural networks at multiple levels including weights, connections, and the number and types of neurons.
	www.aticourses.com /neural_evolutionary_computation.htm (732 words)

	Evolutionary algorithm - Wikipedia, the free encyclopedia
		In artificial intelligence, an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm.
		Evolutionary algorithms consistently perform well approximating solutions to all types of problems because they do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, genetics, operations research, robotics, social sciences, physics, and chemistry.
		Apart from their use as mathematical optimizers, evolutionary computation and algorithms have also been used as an experimental framework within which to validate theories about biological evolution and natural selection, particularly through work in the field of artificial life.
	en.wikipedia.org /wiki/Evolutionary_algorithm (747 words)

	An Overview of Evolutionary Computation
		Evolutionary computation uses computational models of evolutionary processes as key elements in the design and implementation of computer-based problem solving systems.
		In this paper we provide an overview of evolutionary computation, and describe several evolutionary algorithms that are currently of interest.
		There are a variety of evolutionary computational models that have been proposed and studied which we will refer to as evolutionary algorithms.
	www.cs.uwyo.edu /~wspears/overview/ecml93.all.html (5892 words)

	Genetic Algorithms and Evolutionary Computation
		The evolutionary postulate of common descent has aided the development of new medical drugs and techniques by giving researchers a good idea of which organisms they should experiment on to obtain results that are most likely to be relevant to humans.
		Evolutionary algorithms, on the other hand, have proven to be effective at escaping local optima and discovering the global optimum in even a very rugged and complex fitness landscape.
		About 25 years ago, chess-playing computer programs were in their infancy; a computer had only recently entered even a regional competition for the first time, although it did not win (Sagan 1979, p.286).
	www.talkorigins.org /faqs/genalg/genalg.html (18080 words)

	Metaheuristics and Evolutionary Computation
		Evolutionary Algorithms (EAs), in particular, comprise a variety of related algorithms that are based on the processes of evolution in nature.
		Especially the combination of evolutionary algorithms with problem specific heuristics, local-search based techniques, approximation methods and exact techniques enables often highly efficient optimization algorithms for manyfold areas of application.
		For an introduction to metaheuristics and evolutionary algorithms, you might take a look at a copy of the slides of this lecture (in German).
	aragorn.ads.tuwien.ac.at /research/EA (2668 words)

	CSE 584 -- Evolutionary Computation
		The second half will be devoted to completing a larger project involving evolutionary computation.
		Introduction to evolutionary computation; a simple genetic algorithm in C. Required reading: Textbook, Chapter 1; handout of C code.
		Evolutionary computation applied to data analysis and forecasting.
	web.cecs.pdx.edu /~mm/cse584-2003.html (767 words)

	Genetic Argonaut: Evolutionary Computation Classics - Vol. I (Site not responding. Last check: 2007-10-13)
		Computers at that time were not so available as today and their computational power was very weak.
		The two Evolutionary Computation communities, in USA and Germany, only would meet at PPSN in Dortmund in 1990 (however, would be fair to say that David E. Goldberg and Yuval Davidor were in Germany in 1989 for a conference, at the invitation of Hans-Paul Schwefel.
		Computers were not yet available in larger numbers, and if so, their power was rather weak.
	geneticargonaut.blogspot.com /2006/03/evolutionary-computation-classics-vol.html (4350 words)

	Amazon.ca: Evolutionary Computation in Bioinformatics: Books: Gary B. Fogel,David W. Corne (Site not responding. Last check: 2007-10-13)
		The fourteen chapters that follow are written by leading computer scientists and biologists who examine successful applications of evolutionary computation to various problems in the biological sciences.
		However the main reference is not yet available as it is very recent and in press, and, although the authors do include a fairly lengthy discussion of computational experiments, without a detailed description of the algorithm or source code, their results cannot be checked or validated.
		The contrast between optimization theory and evolutionary algorithms is a common theme in the book, with emphasis on the use of evolutionary algorithms to design scoring schemes for sequence alignment where optimization issues can be ignored.
	www.amazon.ca /Evolutionary-Computation-Bioinformatics-Gary-Fogel/dp/1558607978 (1355 words)

	Interactive Evolutionary Computation
		There is a history of research relating to interactive evolutionary computing which, in the main, relates to partial or complete human evaluation of the fitness of solutions generated from evolutionary search.
		Parmee and J. Abraham, Advanced Computation in Design and Decision-making, CEMS University of the West of England, Bristol, UK The presentation speculates upon the development of human-centric evolutionary conceptual design systems that support implicit learning through the succinct visual presentation of data relating to both variable and objective space.
		The Workshop will again be held at the Genetic and Evolutionary Computing Conference this year to be held at the Renaissance Hotel in Seattle, Washington, USA, July 8th - 12th 2006 (http://www.sigevo.org/gecco-2006).
	www.ad-comtech.co.uk /Workshops.htm (4111 words)

	Evolutionary computation - Wikipedia, the free encyclopedia
		In computer science evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems.
		Whereas evolutionary algorithms generally only involve techniques implementing mechanisms such as reproduction, mutation, recombination, natural selection and survival of the fittest, evolutionary computation can be loosely recognised by the following criteria:
		evolutionary algorithms (comprising genetic algorithm, evolutionary programming, evolution strategy, genetic programming and learning classifier systems)
	en.wikipedia.org /wiki/Evolutionary_computation (143 words)

	EVOLUTIONARY COMPUTATION: THEORY AND APPLICATIONS (Site not responding. Last check: 2007-10-13)
		Evolutionary computation is the study of computational systems which use ideas and get inspiration from natural evolution and adaptation.
		This book is devoted to the theory and application of evolutionary computation.
		A strong connection is established between evolutionary algorithms and traditional search algorithms.
	www.worldscibooks.com /compsci/2792.html (319 words)

	genetic-programming.org-Home-Page (via CobWeb/3.1 planetlab2.netlab.uky.edu) (Site not responding. Last check: 2007-10-13)
		The evolutionary search uses the Darwinian principle of natural selection (survival of the fittest) and analogs of various naturally occurring operations, including crossover (sexual recombination), mutation, gene duplication, gene deletion.
		Evolutionary Computation journal (published by The MIT Press and edited by Marc Schoenauer).
		Past GECCO conferences (Genetic and Evolutionary Computation Conferences) for 1999, 2000, 2001, 2002, 2003, and 2004.
	www.genetic-programming.org.cob-web.org:8888 (2651 words)

	UCF Evolutionary Computation Laboratory (Site not responding. Last check: 2007-10-13)
		Welcome to the Evolutionary Computation Laboratory in the School of EECS at the University of Central Florida.
		We are a group of students and faculty members who are interested in studying and understanding the theory and dynamics of evolutionary algorithms.
		Evolutionary algorithms, including genetic algorithms, evolutionary programming, and evolutionary strategies, are search and learning tools which were first developed in the 1960's and 1970's.
	www.cs.ucf.edu /~ecl (102 words)

	Evolutionary Computation
		Evolutionary Computation is the general term for several computational techniques which are based to some degree on the evolution of biological life in the natural world.
		The most widely used form of evolutionary computation are Genetic Algorithms.
		The generation of form using an evolutionary approach by Michael Rosenman (see also the 1997 paper) and A Hierarchical Evolutionary Approach to the Generation of Form which presents an evolutionary approach to design using a hierarchical decomposition/aggregation growth model for architectual applications.
	www.red3d.com /cwr/evolve.html (1944 words)

	Evolutionary Computation Repository (Site not responding. Last check: 2007-10-13)
		Evolutionary computation (EC) encompasses genetic algorithms (GA), evolution strategies (ES), evolutionary programming (EP), genetic programming (GP), and classifier systems (CS).
		These pages are part of the WWW site of the department of formal concepts in the computer science faculty of the University of Stuttgart.
		This is under construction, but will contain keywords as: genetic algorithm, GA, evolutionary computation, EC, evolutionary programming, EP, genetic programming, GP, evolution strategy, ES, evolutionary algorithm, softcomputing, evolutionary design, molecular computing, DNA computing, particle swarms, ant colonies, mutation operator, crossover, recombination operator, adaptation, self-adaptation, selection, fitness proportional, representation, etc.
	www.fmi.uni-stuttgart.de /fk/evolalg (135 words)

	EC lab Home
		The Evolutionary Computation Laboratory at George Mason University consists of a group of faculty, students, and affiliated scientists and engineers who conduct theoretical and applied research in the area of Evolutionary Computation.
		Such models can be used to better understand existing evolutionary systems (such as bacterial resistance to penicillin) or can be used to make computer systems themselves more robust, flexible and adaptive (such as a stock market trading program).
		Additional information about the field of Evolutionary Computation can be found in a variety of places on the web including The Hitch-Hiker's Guide to Evolutionary Computation.
	cs.gmu.edu /~eclab (250 words)

	Amazon.ca: Evolutionary Computation 1: Books: Thomas Back,Zbigniew Michalewicz,David B. Fogel (Site not responding. Last check: 2007-10-13)
		It is intended to be used by individual researchers and by teachers and students in the expanding field of evolutionary computation.
		If you need a fast coverage of the literature in evolutionary computation, this is the book.
		I used both volumes for a course I teach in evolutionary computation.
	www.amazon.ca /Evolutionary-Computation-Basic-Algorithms-Operators/dp/0750306645 (535 words)

	Evolutionary Quantum Computation
		Abstract: An evolutionary quantum computer (EQC) is a physical system that maintains an internal ensemble of macroscopic "quantum subsystems" manifesting significant quantum indeterminacy, with the property that the ensemble of quantum subsystems is continually changing in such a way as to optimize some measure of the emergent patterns between the system and its environment.
		The promise of quantum computing is that, while a system is uncollapsed, it can carry out more computing than a collapsed system, because, in a sense, it is computing in an infinite number of universes at once.
		However, the "average- case" behavior is a different story: on average, quantum computers are in principle capable of vastly outperforming ordinary digital computers on a great number of important problem-solving tasks, including pattern-recognition and code- cracking (Deutsch, 1985).
	www.goertzel.org /dynapsyc/1997/Qc.html (5674 words)

	Amazon.com: Evolutionary Computation 2 (Advanced Algorithms and Operators): Books: Thomas Baeck,D.B Fogel,Z Michalewicz (Site not responding. Last check: 2007-10-13)
		Evolutionary Computation 1: Basic Algorithms and Operators (Evolutionary Computation) by Thomas Baeck
		Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Third Edition (IEEE Press Series on Computational Intelligence) by David B. Fogel
		The focus is on fitness evaluation, constraint-handling techniques, population structures, advanced techniques in evolutionary computation and implementation of evolutionary algorithms.
	www.amazon.com /Evolutionary-Computation-Advanced-Algorithms-Operators/dp/0750306653 (987 words)

	Amazon.com: Handbook of Evolutionary Computation (Computational Intelligence Library): Books: Thomas Back,David B. ... (Site not responding. Last check: 2007-10-13)
		Many scientists and engineers now use the paradigms of evolutionary computation (genetic algorithms, evolution strategies, evolutionary programming, genetic programming, classifier systems, and combinations or hybrids thereof) to tackle problems that are either intractable or unrealistically
		This work is intended to become the standard reference resource for the evolutionary computation community.
		This book is an edited collection of chapters written by knowledgeable researchers in the field of evolutionary computation.
	www.amazon.com /Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921 (751 words)

	Interactive evolutionary computation - Wikipedia, the free encyclopedia
		Interactive evolutionary computation (IEC) or Aesthetic Selection is a general term for methods of evolutionary computation that use human evaluation.
		Usually human evaluation is necessary when the form of fitness function is not known (for example, visual appeal or attractiveness) or the result of optimization should fit a particular user preference (for example, taste of coffee or color set of the user interface).
		Takagi, H. Interactive Evolutionary Computation: Fusion of the Capacities of EC Optimization and Human Evaluation.
	en.wikipedia.org /wiki/Interactive_evolutionary_computation (577 words)

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