Genetic Algorithms: An Overview with Applications in Evolvable Hardware

The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” (i.e., minimizes the cost function). The fundamental principle of natural selection as the main evolutionary principle has been formulated by Charles Darwin, without any knowledge about genetic mechanism. After many years of research, he assumed that parents qualities mix together in the offspring organism. Favorable variations are preserved, while the unfavorable are rejected. There are more individuals born than can survive, so there is a continuous struggle for life. Individuals with an advantage have a greater chance for survive i.e., the “survival of the fittest”. This theory arose serious objections to its time, even after the discovering of the Mendel’s laws, and only in 1920s “was it proved that Mendel’s genetics and Darwin’s theory of natural selection are in no way conflicting and that their happy marriage yields modern evolutionary theory” (Michalewicz, 1996).

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