Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
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Genetic algorithms (GAs) are computational search, learning, optimization, and modeling methods, loosely inspired by biological evolution. Imitating the mechanisms of evolution has appealed to computer scientists from nearly the beginning of the computer age. Very roughly speaking, evolution can be viewed as searching in parallel among an enormous number of possibilities for "solutions" to the problem of survival in an environment where the solutions are particular designs for organisms. Viewed from a high level, the "rules" of evolution are remarkably simple: species evolve by means of heritable variation (via mutation, recombination, and other operators), followed by natural selection in which the fittest tend to survive and reproduce, thus propagating their genetic material to future generations. Yet these simple rules are thought to be responsible, in large part, for the extraordinary variety and complexity we see in the biosphere. Seen in this light, the mechanisms of evolution can inspire computational search methods for finding solutions to hard problems in large search spaces or for designing complex systems automatically.