Genetic Invariance: A New Approach to Genetic Algorithms

Michael Lewchuk Masters Thesis April 1992

Postscrip available here


Genetic algorithms are adaptive search algorithms which generate and test a population of individuals, where each individual corresponds to a solution. They then adapt to the information obtained from testing, seeking superior solutions by selecting and combining solutions of above average value. As the number of superior individuals in the population increases, the number of inferior individuals decreases. This thesis introduces Genetic Invariance, a similar family of generate and test problem solvers which uses a different selection and replacement strategy. In the best case, it achieves superior solutions without eliminating inferior characteristics. Although characteristics may initially be associated with inferior solutions, they may prove to be superior when combined with other particular characteristics. Mathematical analysis of lower bounds of Genetic Invariance on a simple function is given, and several properties of Genetic Invariance are explained using this analysis. A comparison and contrast is done to show how the two selection strategies achieve optimization in different ways. An analysis of the assumption and strategies of each system explains likely beneficial and detrimental effects of each system, while empirical analysis is given which demonstrates these effects. Together, they show each system's features and drawbacks. Keywords: genetic algorithms, gene invariance, one max function analysis