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Topic: Genetic algorithm

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Selection (genetic algorithm)

Interactive genetic algorithms

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List of algorithms

Traveling salesman problem

Genetic programming

Tournament selection

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Metropolis algorithm

Cluster analysis

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Timeline of algorithms

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	SPACE.com -- Darwinian Selection of Satellite Orbits for Military Use
		The genetic algorithm selected the best-performing constellations by interchanging variables such as how far apart the satellites are from each other, the heading of the satellites as they cross the equator, and how high they are above the Earth's surface.
		As with evolution, genetic algorithms are known for producing unexpected results.
		But in the best-performing configurations discovered by the genetic algorithm, the satellites were not spaced at equal distances.
	www.space.com /news/darwin_satellites_011016.html (566 words)

	Genetic algorithm - Wikipedia, the free encyclopedia
		Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).
		Genetic algorithms are implemented as a computer simulation in which a population of abstract representations (called chromosomes or the genotype) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions.
		As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as recombination is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in.
	en.wikipedia.org /wiki/Genetic_algorithm (3572 words)

	Genetic Algorithm
		Genetic algorithms, first proposed by Holland in 1975 [64], are a class of computational models that mimic natural evolution to solve problems in a wide variety of domains [65].
		Genetic algorithms are particularly suitable for solving complex optimization problems and for applications that require adaptive problem solving strategies.
		A future implementation using simulated annealing is very probable, but the genetic algorithm was chosen, as the first implementation, because of its novelty and because it has shown better results than simulated annealing [8].
	java.icmc.sc.usp.br /dilvan/thesis.phd/genetic.html (1981 words)

	Genetic Algorithms and Evolutionary Computation
		Finally, one of the qualities of genetic algorithms which might at first appear to be a liability turns out to be one of their strengths: namely, GAs know nothing about the problems they are deployed to solve.
		Genetic algorithms, which are "known to be able to find good solutions to very complex problems of high dimensionality" (p.75) without needing to be supplied with domain-specific information on the topology of the search landscape, were applied successfully to this problem.
		Genetic programming was given a set of standard mathematical operators to work with, as well as a set of boolean amino-acid-detecting functions that return +1 if the amino acid at a given position is the amino acid they detect and otherwise return -1.
	www.talkorigins.org /faqs/genalg/genalg.html (18080 words)

	Genetic Algorithm - GPWiki
		The basic genetic algorithm attempts to evolve traits that are optimal for a given problem.
		This is the main part of the genetic algorithm, where the strong survive and the weak perish.
		Now that you are familiar with the general idea of a genetic algorithm, this summary of the procedure should cement the concept nicely in your mind.
	gpwiki.org /index.php/Genetic_Algorithm (1174 words)

	Generation 5: Artificial Intelligence Repository - Introduction to Genetic Algorithms
		Genetic algorithms are not too hard to program or understand, since they are biological based.
		A genetic algorithm can be used to 'evolve' an expression tree to create a very close fit to the data.
		Al Biles uses genetic algorithms to filter out 'good' and 'bad' riffs for jazz improvisation, the military uses GAs to evolve equations to differentiate between different radar returns, stock companies use GA-powered programs to predict the stock market.
	library.thinkquest.org /18242/ga.shtml (1096 words)

	Intro to Genetic Algorithms (Site not responding. Last check: 2007-10-31)
		Genetic algorithms were formally introduced in the United States in the 1970s by John Holland at University of Michigan.
		The genetic algorithm then creates a population of solutions and applies genetic operators such as mutation and crossover to evolve the solutions in order to find the best one(s).
		The three most important aspects of using genetic algorithms are: (1) definition of the objective function, (2) definition and implementation of the genetic representation, and (3) definition and implementation of the genetic operators.
	lancet.mit.edu /~mbwall/presentations/IntroToGAs (200 words)

	UAMIS AI Group - Genetic Algorithm Optimizer (Site not responding. Last check: 2007-10-31)
		Initially, when the genetic algorithm program is started for the first time or reset, all the agents in the population are given random values for their genes which results in random X values being represented since the genes determine the X position.
		Genetic algorithms are excellent for quickly finding an approximate global maximum or minimum value.
		The two major problems with creating genetic algorithms are in converting a problem domain into genes (bit patterns) and creating an effective evaluation function.
	ai.bpa.arizona.edu /~mramsey/ga.html (1118 words)

	Crossover (genetic algorithm) - Wikipedia, the free encyclopedia
		In genetic algorithms, crossover is a genetic operator used to vary the programming of a chromosome or chromosomes from one generation to the next.
		It is an analogy to reproduction and biological crossover, upon which genetic algorithms are based.
		Larry J. Eshelman, The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination, in Gregory J. Rawlins editor, Proceedings of the First Workshop on Foundations of Genetic Algorithms.
	en.wikipedia.org /wiki/Crossover_(genetic_algorithm) (484 words)

	Linux: Tuning The Kernel With A Genetic Algorithm \| KernelTrap
		Genetic algorithms as used in machine learning are modeled after the process of evolution as observed in nature, and are a field within the science of artificial intelligence.
		And as i said in a previous comment: using a genetic algorithm for tweaking schedulers is conceptually flawed, because the nature of the problem they both need to solve is exactly the same.
		Throughput, latency and fairness maximation for any workload is the goal, and whether the selection mechanism in the genetic algorithm solves that by tuning the settings of a dumb scheduler or the scheduler fulfills its purpose only moves the place where the decisions have to be made.
	www.kerneltrap.org /node/4493 (4910 words)

	Implementing a Genetic Algorithm in C# and .NET (Site not responding. Last check: 2007-10-31)
		Surprisingly enough Genetic Algorithms have been around before the dawn of man. The idea of using Genetic Algorithms has emerged from the observation of life and evolution.
		The hardest part of executing a genetic algorithm is coming up with a fitness function once the appropriate classes are in place.
		Genetic algorithms are a great way to solve problems through a trial an error process that progresses very quickly.
	www.c-sharpcorner.com /Code/2002/July/GeneticAlgorithm.asp (1173 words)

	Introduction to Genetic Algorithm
		The diversity of the genetic pool is then too reduced to allow the genetic algorithm to progress.
		Genetic algorithms are original systems based on the supposed functioning of the Living
		We are in a stochastic system and a genetic pool may be too far from the solution, or for example, a too fast convergence may halt the process of evolution.
	www.rennard.org /alife/english/gavintrgb.html (2011 words)

	FAQ: comp.ai.genetic part 2/6 (A Guide to Frequently Asked Questions) - Q1.1: What's a Genetic Algorithm (GA)?
		The GENETIC ALGORITHM is a model of machine learning which derives its behavior from a metaphor of the processes of EVOLUTION in nature.
		GENETIC ALGORITHMs are used for a number of different application areas.
		When the GENETIC ALGORITHM is implemented it is usually done in a manner that involves the following cycle: Evaluate the FITNESS of all of the INDIVIDUALs in the POPULATION.
	www.cs.cmu.edu /Groups/AI/html/faqs/ai/genetic/part2/faq-doc-2.html (804 words)

	Citations: An analysis of reproduction and crossover in a binary-coded genetic Algorithm - Bridges, Goldberg ...
		Modeling using schemata is the equivalent technique for string representation genetic algorithms; they model the behavior of the GA at a subset of the string positions.
		The Algebra of Genetic Algorithms - Radcliffe (1994)
		Hybridised Genetic Algorithm and K-Nearest Neighbour for Rainfall..
	citeseer.ist.psu.edu /context/264463/0 (1850 words)

	Genetic Algorithm Encodings (Site not responding. Last check: 2007-10-31)
		In genetic algorithms, inversion is implemented by changing the encoding to carry along a tag which identifies the position of a bit in the string [Goldberg, 1989b].
		A genetic algorithm using classical crossover is less likely to be misled than a genetic algorithm using adaptive crossover since classical crossover ignores information used by adaptive crossover and is thus immune to misdirection in this information.
		A designer genetic algorithm's performance is compared with that of a classical GA on the adder and parity problems.
	www.cse.unr.edu /~sushil/papers/thesis/thesishtml/node4.html (4382 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)

	[No title]
		GP is one instance of the class of techniques called evolutionary algorithms, which are based on insights from the study of natural selection and evolution.
		Genetic programming and genetic algorithms are two different evolutionary algorithms.
		While this implementation meets all of the essential requirements to qualify as genetic programming, in its present state there is plenty of room for the solution to be augmented and improved.
	msdn.microsoft.com /msdnmag/issues/04/08/GeneticAlgorithms/default.aspx (5759 words)

	Genetic Algorithm
		A genetic algorithm is simply the algorithm used to simulate evolution.
		JohnHolland's 'classical' genetic algorithm specifically used raw bitstrings as the candidate solutions, a problem-specific evaluation function depending on the problem, and simple bit-flip mutations and bitstring swapping for cross-over.
		I was originally fascinated by genetic algorithms for their simple appeal of beng able generally to solve any problem no matter whether an algorithmic solution already existed for it.
	c2.com /cgi/wiki?GeneticAlgorithm (2629 words)

	Genetic Algorithms - Nature's Way
		Yes, this is the general field known as Evolutionary Computation, of which the Genetic Algorithm is the basic technique and can be further expanded to remove the need for an underlying program (in the field of Genetic Programming).
		This technique can rapidly covers the space of all possible options and converge on a solution that is beyond the ability of all but the best human programmers, in areas where no conventional solution techniques exist.
		In the Genetic Programming extension, the genes are the valid statements within a computer language and the chromosome in this case is itself the program.
	www.calresco.org /genetic.htm (1285 words)

	Genetic Algorithm Software - GA Software
		Genetic Server and Genetic Library provide general purpose APIs for genetic algorithm design.
		Genetic Server is an ActiveX component that can be used to easily build custom genetic applications in Visual Basic.
		Genetic Library is a C++ library that can be used for building custom genetic applications in Visual C++.
	www.nd.com /genetic (285 words)

	Genetic Algorithm & Genetic Programming
		Icosystem claims that evolutionary algorithms expose ideas to a kind of natural selection, allowing users to 'reach beyond the limits of their imagination.' But the notion that serendipity might produce better results than thinking and planning left me suspicious.
		Evolutionary algorithms, also known as genetic algorithms or GAs, take their cue from biological evolution, which can turn a crawling reptile into a soaring bird without any kind of forward-looking blueprint.
		"Genetic algorithms and their relations, which fall under the umbrella term evolutionary computing, are being harnessed to optimize designs of all sorts.
	www.aaai.org /AITopics/html/genalg.html (4299 words)

	Genetic Algorithms Demo
		The genetic algorithm is described fully by John Holland in his book, "Adaptation in Natural and Artificial Systems." More popular accounts can be found in the books "Complexity" by M. Mitchell Waldrop and "Artificial Life" by Steven Levy.
		The genetic algorithm can be applied to many different types of problems, but GA uses it to evolve simulated "organisms" called Eaters in a simulated world that contains simulated plants for the Eaters to eat.
		Crossover here means that the chromosomes exchange some genetic material; a random position between 1 and 128 is chosen and all data on the chromosomes after that position are swapped between the two chromosomes.
	math.hws.edu /xJava/GA (1495 words)

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