A naive genetic approach for non-stationary constrained problems

Abstract

An algorithm to be effective for solving non-stationary problems should be robust, adaptive to the changing environment and efficient. Genetic algorithms (GAs) are increasingly being used to solve non-stationary problems. We use GA with a new approach of gene induction (Bhatia and Basu in Soft Comput 8(1):1-9, 2003) to solve non-stationary constrained problems. The approach combines high value genes to form chromosomes from the initial population itself. The efficacy of the method is demonstrated on non-stationary versions of 0/1 knapsack and pure-integer programming problems. The results obtained with the approach are compared with those obtained with feedback thermodynamical genetic algorithm (FTDGA) (Mori et al. in 5th parallel problem solving from nature, number 1498 in LNCS, pp 149-157, 1998). It shows that gene-induction approach is more accurate and requires less time compared to the FTDGA. © Springer-Verlag 2005.