60.2. Genetic Algorithms

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 60.1 illustrates these steps.

Figure 60.1. 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).