Genetic Algorithms

Abstract

This chapter provides a detailed analysis of the Genetic Algorithm (GA) search technique. The relationship between the choice of GA operators and the likely success of a GA search is investigated. A new style of GA is introduced that makes use of multiple randomly selected representations during the course of a run. The Morphic GAs use transmigration and transmutation operators to adjust the encoding of individuals within the population via base changes. It is shown that contrary to conventional GA analysis there is no formal justification for preferring binary representations to those of higher base alphabets. Moreover, higher base alphabets provide a simple method of dynamically re-mapping the search space. A series of experiments compares Morphic GAs to the conventional binary Holland GA on a range of standard test functions.