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Topic: Fitness (genetic algorithm)

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	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, natural selection, and recombination (or crossover).
		Genetic algorithms are typically implemented as a computer simulation in which a population of abstract representations (called chromosomes) of candidate solutions (called individuals) to an optimization problem evolves toward better solutions.
		In each generation, the fitness of the whole population is evaluated, multiple individuals are stochastically selected from the current population (based on their fitness), modified (mutated or recombined) to form a new population, which becomes current in the next iteration of the algorithm.
	en.wikipedia.org /wiki/Genetic_algorithm (2316 words)

	Evolution. Everything you wanted to know about Evolution but had no clue how to find it.. Learn about Evolution here! (Site not responding. Last check: 2007-11-06)
		Genetic drift describes changes in allele frequency that cannot be ascribed to selective pressures, but are due instead to events that are unrelated to inherited traits.
		The relative importance of natural selection and genetic drift in determining the fate of new mutations also depends on the population size and the strength of selection: when N times s (population size times strength of selection) is small, genetic drift predominates.
		Population genetics is the branch of biology that provides the mathematical structure for the study of the process of microevolution.
	encyclopedia.lockergnome.com /s/b/Evolution (4014 words)

	Genetic algorithm (Site not responding. Last check: 2007-11-06)
		Genetic algorithms are a particular class of evolutionary algorithms.
		John Holland was the pioneering founder of much of today's work in genetic algorithms, which has moved on from a purely theoretical subject (though based on computer modelling) to provide methods which can be used to actually solve some difficult problems today.
		Genetic programming algorithms typically require running time that is orders of magnitude greater than that for genetic algorithms, but they may be suitable for problems that are intractable with genetic algorithms.
	www.sciencedaily.com /encyclopedia/genetic_algorithm (1930 words)

	Cultured Perl: Genetic algorithms applied with Perl
		It is fitting that one of the most intriguing algorithms to come about in the 20 th century is the genetic algorithm.
		The fitness formula is the most-used function in the genetic algorithm (it will be invoked (population size) x (generations times)), so you should make it as simple and as fast as possible.
		Fitness was calculated as: 2 for each letter in the DNA, plus the frequency of that letter in the dictionary, plus 2^N for every dictionary word of length N in the DNA.
	www-106.ibm.com /developerworks/linux/library/l-genperl (2386 words)

	Definition of Evolution (Site not responding. Last check: 2007-11-06)
		In the context of life science, evolution is a change in the genetic makeup of a population of interbreeding individuals within a species.
		Comparison of the genetic sequence of organisms reveals that organisms that are phylogenetically close have a higher degree of sequence similarity than organisms that are phylogenetically distant.
		Further evidence for common descent comes from genetic detritus such as pseudogenes, regions of DNA which are orthologous to a gene in a related organism, but are no longer active and appear to be undergoing a steady process of degeneration.
	www.wordiq.com /definition/Evolution (4041 words)

	Genetic Algorithm Experiment (Site not responding. Last check: 2007-11-06)
		Fitness Roulette : The probability of an individual being selected in the population is equal to the fitness value normalized with respect to the total fitness of the population.
		At this point, the fitness values of the individuals are very close and none of them has a distinct advantage to be selected for reproduction.
		One individual is selecting using fitness roulette, but the mate is found by travelling up the family tree a random distance and then randomly travelling back down to the current generation.
	www.oursland.net /projects/PopulationExperiment (780 words)

	Evolution Encyclopedia Article, History, Biography @ Local Color Art (Site not responding. Last check: 2007-11-06)
		genetics in the 1940s, evolution has been defined more specifically as a change in the frequency of alleles in a population from one generation to the next.
		Genetic testing has shown that humans and chimpanzees have most of their DNA in common.
		Mutations are permanent, transmissible changes to the genetic material (usually DNA or RNA) of a cell, and can be caused by "copying errors" in the genetic material during cell division and by exposure to radiation, chemicals, or
	search.localcolorart.com /search/encyclopedia/Evolution (4649 words)

	Citations: The genetic algorithm and the structure of the fitness landscape - Manderick, de Weger, Spiessens ... (Site not responding. Last check: 2007-11-06)
		Citations: The genetic algorithm and the structure of the fitness landscape - Manderick, de Weger, Spiessens (ResearchIndex)
		He devoted attention to the correlation between mean parental fitness and mean offspring fitness, and indicated the possible usefulness of this measure to assess the role of an operator on the NK landscapes and the TSP problems.
		The genetic algorithm and the structure of the fitness landscape.
	citeseer.ist.psu.edu /context/11378/0 (3215 words)

	Vertices Wint95: Genetic Algorithms
		Whereas hill-climbing and its relatives require domain-specific information (e.g., partial derivatives) to guide their searches, the genetic algorithm requires only two things: (1) a means of representing possible solutions and (2) an objective function evaluator--a function which maps a value from the domain of possible solutions to a scalar value.
		Using an individual's objective function as a measure of how "fit" that individual is within its environment (the problem domain,) the genetic algorithm simulates nature's survival of the fittest, essentially forcing the evolution of an optimal creature.
		He recognized the broad applicability of genetics-based algorithms for optimization purposes, and this insight formed the basis for the modern notion of a genetic algorithm.
	www.duke.edu /vertices/update/win95/genalg.html (2545 words)

	Machine Learning for Information Retrieval: Neural Networks, Symbolic Learning, and Genetic Algorithms
		Both algorithms were able to use user-supplied samples of desired documents to construct decision trees of important keywords which could represent the users' queries.
		Genetic algorithms use a vocabulary borrowed from natural genetics in that they talk about genes (or bits), chromosomes (individuals or bit strings), and population (of individuals).
		For 1-document and 2-document test cases, their initial fitness tended to be higher due to the smaller sample size (see Column 2 of Table 4).
	ai.bpa.arizona.edu /papers/mlir93/mlir93.html (12954 words)

	CodeGuru: Genetic Algorithm and Traveling Salesman Problem (Site not responding. Last check: 2007-11-06)
		I am not a genetic algorighm (GA) guru and I do not have any degree in GA so this article can't be used as a GA GA tutorial.
		"Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence.
		"Genetic algorithms are neat, but they do come with their own set of problems.
	codeguru.earthweb.com /Cpp/misc/misc/article.php/c3795 (1919 words)

	FAQ: comp.ai.genetic part 5/6 (A Guide to Frequently Asked Questions) (Site not responding. Last check: 2007-11-06)
		It features a class library for genetic algorithm programming, but, from the user point of view, is a genetic algorithm application generator.
		This is done with two genetic algorithms, the first one develops the topology of the network, the second one adjusts the weights.
		Genetic Server is an ActiveX component designed to be used within a Visual Basic (or VBA) application and Genetic Library is a C++ library designed to be used within a Visual C++ application.
	www.faqs.org /faqs/ai-faq/genetic/part5 (10117 words)

	CodeGuru: Genetic Algorithm and Traveling Salesman Problem (Site not responding. Last check: 2007-11-06)
		A value for fitness is assigned to each solution (chromosome) depending on how close it actually is to solving the problem (thus arriving to the answer of the desired problem).
		We can't use a traditional presentation and algorithms for TSP problem, because every city must be unique in gene, and can't be duplicated.
		For example, if the best chromosome fitness is 90% of all the roulette wheel then the other chromosomes will have very few chances to be selected.
	www.codeguru.com /Cpp/misc/misc/article.php/c3795 (1919 words)

	[No title]
		A survey paper entitled "Genetic Algorithm Programming Environments" was published in IEEE Computer in the February 1994 issue.
		GECO: GECO (Genetic Evolution through Combination of Objects) is an extensible, object-oriented framework for prototyping GENETIC ALGORITHMs in Common Lisp.
		WOLF: This is a simulator for the G/SPLINES (genetic spline models) algorithm which builds spline-based functional models of experimental data, using CROSSOVER and MUTATION to evolve a POPULATION towards a better fit.
	sunsite.auc.dk /corewar/faq/comp.ai.genetic/part5 (9482 words)

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