Genetic Invariance � A New Paradigm for Genetic Algorithm Design

This paper presents some experimental results and analyses of the gene invariant genetic algorithm GIGA Although a subclass of the class of genetic algorithms this algorithm and its variations represent a unique approach with many interesting results The primary distin guishing feature is that when a pair of o spring are created and chosen as worthy of membership in the population they replace their parents With no mutation this has the e ect of maintaining the original genetic material over time although it is reorganized In this paper no mutation is allowed The only genetic operator used is crossover Several crossover operators are experimented with and analyzed The notion of a family is introduced and di erent selec tion methods are analyzed Tests using simple functions the De Jong ve function test suite and several deceptive functions are reported GIGA performs as well as traditional GAs and sometimes better The evidence indicates that this method makes more e ective use of the crossover operator in part because it never loses genetic material and thus has greater scope for recombination A new view of crossover search space structures and approaches to analysis are presented Traditional methods of analysis for GAs do not seem to apply since GIGAs cannot be said to give increased trials to the best schemata in the usual sense However the analysis of crossover search space structures may have applications in traditional GA analysis Supported by Natural Sciences and Engineering Research Council Grant No OGP Department of Computing Science University of Alberta Edmonton Alberta Canada T G H email joe cs ualberta ca This paper is available via ftp thorhild cs ualberta ca in pub GIGA