62.2. Genetic Algorithms

62.2. Genetic Algorithms
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+ The genetic algorithm (GA) is a heuristic optimization method which + operates through randomized search. The set of possible solutions for the + optimization problem is considered as a + population of individuals. + The degree of adaptation of an individual to its environment is specified + by its fitness. +

+ The coordinates of an individual in the search space are represented + by chromosomes, in essence a set of character + strings. A gene is a + subsection of a chromosome which encodes the value of a single parameter + being optimized. Typical encodings for a gene could be binary or + integer. +

+ Through simulation of the evolutionary operations recombination, + mutation, and + selection new generations of search points are found + that show a higher average fitness than their ancestors. Figure 62.1 + illustrates these steps. +

Figure 62.1. Structure of a Genetic Algorithm

+ According to the comp.ai.genetic FAQ it cannot be stressed too + strongly that a GA is not a pure random search for a solution to a + problem. A GA uses stochastic processes, but the result is distinctly + non-random (better than random). +

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