Genetic Invariance : A New Paradigm for GeneticAlgorithm

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 distinguishing feature is that when a pair of oospring are created and chosen as worthy of membership in the population they replace their parents. With no mutation this has the eeect 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 diierent selection 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 eeective 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.