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Genetic Algorithms: Genetic Algorithm, Genetic Programming, Mutation, Weasel Program, Mutation Testing, Quality Control and Genetic Algorithms
Chapters: Genetic Algorithm, Genetic Programming, Mutation, Weasel Program, Mutation Testing, Quality Control and Genetic Algorithms, Neuroevolution of Augmenting Topologies, Population-Based Incremental Learning, Edge Recombination Operator, Crossover, Clonal Selection Algorithm, Fitness Approximation, Cultural Algorithm, Promoter Based Genetic...
Chapters: Genetic Algorithm, Genetic Programming, Mutation, Weasel Program, Mutation Testing, Quality Control and Genetic Algorithms, Neuroevolution of Augmenting Topologies, Population-Based Incremental Learning, Edge Recombination Operator, Crossover, Clonal Selection Algorithm, Fitness Approximation, Cultural Algorithm, Promoter Based Genetic Algorithm, Genetic Fuzzy Systems, Genetic Algorithm in Economics, Hyperneat, Fitness Proportionate Selection, Holland's Schema Theorem, Fitness Function, Swapsc, Chromosome, Sugal, Stochastic Universal Sampling, Tournament Selection, Premature Convergence, Speciation, Gega, Genetic Memory, Truncation Selection, Defining Length, Genetic Operator, Length Schema, Inheritance, Disruption, Gatto. Source: Wikipedia. Pages: 131. Not illustrated. Free updates online. Purchase includes a free trial membership in the publisher's book club where you can select from more than a million books without charge. Excerpt: A genetic algorithm (GA) is a search technique used in computing to find exact or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms (EA) that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover. Genetic algorithms are implemented in a computer simulation in which a population of abstract representations (called chromosomes or the genotype of the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions. Traditionally, solutions are represented in binary as strings of 0s and 1s, but other encodings are also possible. The evolution usually starts from a population of randomly generated individuals and happens in generations. In each generation, the fitness of every individual in the population is evaluated, multiple in...More: http://booksllc.net/?id=40254
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