Genetic Algorithms belong to a larger class of computer-based problem solving systems called Evolutionary Algorithms which use computational models that follow the principles of evolution and heredity in their design and implementation. The other variants of Evolutionary Algorithms are Evolutionary Programming and Evolution Strategies. Genetic Algorithms was developed by John Holland, his colleagues and students at the University of Michigan in early 1970s. Genetic Algorithms deal with optimization problems. Inspired by Darwin’s theory of evolution, Genetic Algorithms employ a repeated process of selection, attrition, and cross-breeding of potential solutions in search of the optimal one to a problem.
This book introduces the fundamental concepts of genetic algorithms in theory followed by a walk-through on the design and implementation of a genetic algorithms project.
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