A Genetic Algorithm Tutorial

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search by hyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.

The tutorial begins with a very low level discussion of optimization to both introduce basic ideas in optimization as well as basic concepts that relate to genetic algorithms. In section 2 a canonical genetic algorithm is reviewed. In section 3 the principle of hyperplane sampling is explored and some basic crossover operators are introduced. In section 4 various versions of the schema theorem are developed in a step by step fashion and other crossover operators are discussed. In section 5 binary alphabets and their effects on hyperplane sampling are considered. In section 6 a brief criticism of the schema theorem is considered and in section 7 an exact model of the genetic algorithm is developed.

The last three sections of the tutorial cover alternative forms of genetic algorithms and evolutionary computational models, including specialized parallel implementations.