genetic algorithm

<< Click to Display Table of Contents >>

Navigation:  »No topics above this level«

genetic algorithm

Return to chapter overview

A genetic algorithm is an optimum search technique based on the concepts of natural selection and survival of the fittest.  It works with a fixed-size population of possible solutions of a problem, called individuals, which are evolving in time.  A genetic algorithm utilizes three principal genetic operators:  selection, crossover, and mutation.


During each step (called a generation) in the reproduction process, the individuals in the current generation are evaluated by a so-called fitness function value, which is a measure of how well the individual solves the problem.  Then each individual is reproduced in proportion to its fitness:  the higher the fitness, the higher its chance to participate in mating (crossover) and to produce an offspring.  A small number of new-born offspring undergo the action of the mutation operator.  After many generations, only those individuals who have the best genetics (from the point of view of the fitness function) survive.  The best individual provides an optimum or near optimum solution to the problem.