Session:

LBP - Late Breaking Papers

Title:

Self-Adaptation in Genetic Algorithms using Multiple Genomic Redundant Representations

Authors:

Maheswara Prasad Kasinadhuni
Michael L. Gargano
Joseph DeCicco
William Edelson

Abstract:

We consider an extension to optimization problems [7, 13, 14] where the element costs are not fixed, but are time dependent. We propose using multiple genomic redundant representations in a self-adapting genetic algorithm (GA) employing various codes with different locality properties. These encoding schemes insure feasibility after performing the operations of crossover and mutation and also ensure the feasibility of the initial randomly generated population (i.e., generation 0). The GAs solving this class of NP hard problems, where costs are not fixed but are time dependent, employ non-locality or locality representations when appropriate (i.e., the GA adapts to its current search needs) which makes the GAs more efficient. A few applications with time dependent costs will also be presented.