
	Where results make sense

Topic: Evolutionary algorithm

Related Topics

Genetic algorithm

Evolvable hardware

Genetic programming

Fitness function

Computer simulation

How to Solve It

Caesar

In the News (Sat 2 Jun 12)

EXECUTIVE POWER

Iran

ANALYSIS

Pakistan

Family compact

	Evolutionary algorithm - Wikipedia, the free encyclopedia
		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.
		Techniques from evolutionary algorithms applied to the modelling of biological evolution are generally limited to explorations of microevolutionary processes, however some computer simulations, such as Tierra and Avida, attempt to model macroevolutionary dynamics.
		A limitation of evolutionary algorithms is their lack of a clear genotype-phenotype distinction.
	en.wikipedia.org /wiki/Evolutionary_algorithm (747 words)

	An Overview of Evolutionary Computation
		There are a variety of evolutionary computational models that have been proposed and studied which we will refer to as evolutionary algorithms.
		More precisely, evolutionary algorithms maintain a population of structures that evolve according to rules of selection and other operators, such as recombination and mutation.
		The differences touch upon almost all aspects of evolutionary algorithms, including the choices of representation for the individual structures, types of selection mechanism used, forms of genetic operators, and measures of performance.
	www.cs.uwyo.edu /~wspears/overview/ecml93.all.html (5892 words)

	Genetic Algorithms and Evolutionary Computation
		In the context of evolutionary algorithms, this is known as the Schema Theorem, and is the "central advantage" of a GA over other problem-solving methods (Holland 1992, p.
		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.
		The evolutionary algorithm began with a population of 15 neural networks with randomly generated weights and biases assigned to each node and link; each individual then reproduced once, generating an offspring with variations in the values of the network.
	www.talkorigins.org /faqs/genalg/genalg.html (18080 words)

	What is a Genetic or Evolutionary Algorithm?
		In a "genetic algorithm," the problem is encoded in a series of bit strings that are manipulated by the algorithm; in an "evolutionary algorithm," the decision variables and problem functions are used directly.
		The use of a population of solutions helps the evolutionary algorithm avoid becoming "trapped" at a local optimum, when an even better optimum may be found outside the vicinity of the current solution.
		A drawback of any evolutionary algorithm is that a solution is "better" only in comparison to other, presently known solutions; such an algorithm actually has no concept of an "optimal solution," or any way to test whether a solution is optimal.
	www.solver.com /gabasics.htm (701 words)

	Metaheuristics and Evolutionary Computation (Site not responding. Last check: 2007-11-05)
		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.
		A memetic algorithm for minimum-cost vertex-biconnectivity augmentation of graphs.
	aragorn.ads.tuwien.ac.at /research/EA (2668 words)

	Publications (Site not responding. Last check: 2007-11-05)
		Assume a coevolutionary algorithm capable of storing and utilizing all phenotypes discovered during its operation, for as long as it operates on a problem; that is, assume an algorithm with a monotonically increasing knowledge of the search space.
		Several issues identified by researchers in the evolutionary robotics community as problematic for the development of ER are alleviated by the use of a large number of robots being evaluated in parallel.
		For evolutionary techniques to scale to the complexities necessary for actual engineering problems, it has been argued that generative systems, where the genotype is an algorithm for constructing the final design, should be used as the encoding.
	www.demo.cs.brandeis.edu /papers/long.html (16009 words)

	GA Playground - Java Genetic Algorithms Toolkit
		An automatic 'Kick': A sensor in the program monitors the evolutionary process, and when it finds that there has not been any advance in the recent N generations (N is user definable), it gives the population a 'Kick' and scrambles it a little (in a user defined manner).
		The evolutionary justification for this mechanism (a justification is not really needed, but anyway), is that identical individuals compete over the same niche, so that although each might possess good genes, the very existence of the others makes it more difficult for him.
		While the evolutionary process is going on, it is possible to log current chunks of data, such as a list of the current population (list of current chromosome strings), or a description of the current mating process (in the format: Father + Mother => Kid => Mutated Kid).
	www.aridolan.com /ga/gaa/gaa.html (2691 words)

	Genetic Algorithms - Evolutionary Algorithms
		We are glad to welcome you to Evolutionary Computing and Genetic algorithms that define Complexity, the next century science.
		Evolutionary computing depends on Darwin’s theory of survival of the fittest.
		Evolutionary Algorithms major difference with other similar algorithms is that they evolve from population of solutions not from a single source of point.
	www.evolutionary-algorithms.com (166 words)

	Evolutionary Algorithm Web Service
		Evolutionary Algorithms are applying on some optimization problems - either theoretical (job shop scheduling, traveling salesman) or practical (economic modeling, pipeline projecting); in machine learning; in game theory; in neural networks; in geometric reasoning etc.
		Darwin’s evolution theory and Mendel’s laws of inheritance are theoretical inspirations of Evolutionary Algorithms.
		Also, Evolutionary Algorithms are easy to hybridize: they easy can be combined with other algorithms that solve specific problem.
	www.matf.bg.ac.yu /~vladaf/EaWeb/index_e.html (1063 words)

	GEATbx: Documentation - Genetic and Evolutionary Algorithm Toolbox for MATLAB
		The documentation of the GEA Toolbox contains a 1 Tutorial, an 2 Introduction to Evolutionary Algorithms and a large 3 Reference section.
		Compared to traditional search and optimization procedures, such as calculus-based and enumerative strategies, Evolutionary Algorithms are robust, globally oriented and generally more straightforward to apply in situations where there is little or no a priori knowledge about the problem to solve.
		As Evolutionary Algorithms require no derivative information or formal initial estimates of the solution, and because they are stochastic in nature, Evolutionary Algorithms are capable of searching the solution space with more likelihood of finding the global optimum.
	www.geatbx.com /docu/index.html (781 words)

	Acovea Overview
		Traditional function-level profiling identifies the algorithms most influential in a program's performance; Acovea is then applied to those algorithms to find the compiler flags and options that generate the fastest code.
		An optimization algorithm may be as simple as removing a loop invariant, or as complex as examining an entire program to eliminate global common sub-expressions.
		As the genetic algorithm cycles through the generations, it refines the best set of options through natural selection; options that produce fast code will occur more often, while adverse option will tend to be winnowed away.
	www.coyotegulch.com /products/acovea (2420 words)

	evolutionary algorithm (Site not responding. Last check: 2007-11-05)
		An evolutionary algorithm maintains a population of structures (usually randomly generated initially), that evolves according to rules of selection, recombination, mutation and survival, referred to as genetic operators.
		EAs are one kind of evolutionary computation and differ from genetic algorithms.
		A GA generates each individual from some encoded form known as a "chromosome" and it is these which are combined or mutated to breed new individuals.
	burks.bton.ac.uk /burks/foldoc/74/39.htm (177 words)

	List of References on Evolutionary Multiobjective Optimization
		Evolutionary Algorithms for Navigation of Underwater Vehicle, in M. Galicki and K. Tchon (editors), Proceedings of the Second International Workshop on Robot Motion and Control, pp.
		Evolutionary algorithms for multicriteria optimization of program module allocations, in M. Koksalan and S. Zionts (eds), 15th International Conference on Multiple Criteria Decision Making (MCDM), Springer-Verlag, Lecture Notes in Economics and Mathematical Sciences, Volume 507, pp.
		Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing, in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.
	www.lania.mx /~ccoello/EMOO/EMOObib.html (10916 words)

	Evolutionary Algorithms 1 Introduction
		Different main schools of evolutionary algorithms have evolved during the last 40 years: genetic algorithms, mainly developed in the USA by J. Holland [Hol75], evolutionary strategies, developed in Germany by I. Rechenberg [Rec73] and H.-P. Schwefel [Sch81] and evolutionary programming [FOW66].
		a short overview of the structure and basic algorithms of evolutionary algorithms is given.
		covers parallel implementations of evolutionary algorithms especially the regional population model employing migration in detail.
	www.geatbx.com /docu/algindex.html (188 words)

	GEATbx - Genetic and Evolutionary Algorithms Toolbox in Matlab - Main Page
		GEATbx: Genetic and Evolutionary Algorithm Toolbox for use with Matlab.
		The GEATbx is the most comprehensive implementation of Evolutionary Algorithms in Matlab.
		However, on the Download page we offer a free Introduction to Evolutionary Algorithms, the Tutorial for the GEATbx, a number of Matlab m-files for everyday work and much more.
	www.geatbx.com (358 words)

	Algorithm::Evolutionary Readme File
		Algorithm::Evolutionary was formerly called OPEAL (hence the name of this site), which is an acronym for Obvious Pearl Evolutionary Algorithm Library; it is about to become an official CPAN distribution (as soon as I figure out how to upload it to PAUSE), that is why I have definitely changed the name.
		It should be easy to program any kind of evolutionary algorithm; all chromosome representations and operators are possible.
		EvoSpec is used as a language for description of algorithms and for representation of the state of an algorithm.
	opeal.sourceforge.net (298 words)

	freshmeat.net: Project details for Algorithm::Evolutionary
		Algorithm::Evolutionary is a flexible set of classes for doing evolutionary computation in Perl, integrated with XML for evolutionary algorithm description.
		It has been distributed algorithms using SOAP, and integrated with the DBI and HTML::Mason libraries.
		It contains an XML dialect for definition of evolutionary algorithms, called EvoSpec; experiments defined using Algorithm::Evolutionary can be completely serialized/deserialized using this language.
	freshmeat.net /projects/ae (166 words)

	J. J. Merelo-Guervós / Algorithm-Evolutionary - search.cpan.org
		Termination condition for an algorithm; checks that the difference of the best to a target is less than a delta
		Checks for termination of an algorithm; terminates when several generations transcur without change
		An operator that performs the simulated annealing algorithm on an individual, using an external freezer.
	search.cpan.org /dist/Algorithm-Evolutionary (304 words)

	evolutionary algorithm from FOLDOC
		EAs are useful for optimisation when other techniques such as gradient descent or direct, analytical discovery are not possible.
		Combinatoric and real-valued function optimisation in which the optimisation surface or fitness landscape is "rugged", possessing many locally optimal solutions, are well suited for evolutionary algorithms.
		Nearby terms: EVGA « evil « evil and rude « evolutionary algorithm » evolutionary computation » evolutionary programming » evolution strategy
	foldoc.org /foldoc/foldoc.cgi?evolutionary+algorithm (185 words)

	EvoWeb - Software - EAML (Evolutionary Algorithm Modelling Language) (Site not responding. Last check: 2007-11-05)
		Therefore such an XML document describes the EA on an abstract level, which supports the rapid design of a huge variety of evolutionary algorithms.
		Moreover, a designed evolutionary-algorithm-model can also be used as compiler input to generate ready to use object-oriented code covering the adequate evolutionary-algorithm-functionality as well as for documentation and exchanging the realised algorithm.
		The basic concept of the model is founded in an element hierarchy, where operators and basic algorithms are handled as parameterized elements of a structural tree and where the structural tree describes the computational flow.
	evonet.lri.fr /evoweb/resources/software/record.php?id=443 (249 words)

	Evolutionary Computation Repository (Site not responding. Last check: 2007-11-05)
		This page contains some information on evolutionary computation which you might find useful.
		Evolutionary computation (EC) encompasses genetic algorithms (GA), evolution strategies (ES), evolutionary programming (EP), genetic programming (GP), and classifier systems (CS).
		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)

	Some Evolutionary Algorithm Sites (Site not responding. Last check: 2007-11-05)
		This list is by no means comprehensive, but it should get you started into some of the bigger genetic algorithm and other evolutionary algorithm sites.
		Genetic Algorithms Research and Applications Group (GARAGe) at Michigan State University
		Yahoo has a small listing of GA topics
	lancet.mit.edu /ga/OtherSites.html (53 words)

	Evolutionary Algorithm Links
		Genetic Algorithms (Evolutionary Algorithms): Repository of Test Problem Generators
		Mathtools.net: The technical computing portal for all your scientific and engineering needs.
		GEATbx: Genetic and Evolutionary Algorithm Toolbox for MATLAB
	www-users.cs.umn.edu /~littau/EA.html (67 words)

	nUCLEAR: The nexus for University College London Evolutionary Algorithms Research (Site not responding. Last check: 2007-11-05)
		nUCLEAR: The nexus for University College London Evolutionary Algorithms Research
		The nexus for University College London Evolutionary Algorithms Research
		For the academic year 2005/2006 Udi Schlessinger is organiser for the group.
	www.cs.ucl.ac.uk /research/nuclear (120 words)

Try your search on: Qwika (all wikis)