Exploiting unexpressed genes for solving large-scaled maximal covering problems

Abstract

We introduce a genetic algorithm incorporating unexpressed genes to solve large-scaled maximal covering problems (MCPs) efficiently. Our genetic algorithm employs new crossover and mutation operators specially designed to work for the chromosomes of set-oriented representation. The unexpressed genes are the genes which are not reflected in the evaluation of the individuals. These genes play the role of preserving information susceptible to be lost by the application of genetic operators but potentially useful in later generations. By incorporating unexpressed genes, the algorithm enjoys the advantage of being able to maintain diversity of the population preventing premature convergence. Experiments with large-scaled real MCP data have shown that our genetic algorithm outperforms simulated annealing and tabu search which are popularly used local neighborhood search algorithms for optimization. © Springer-Verlag Berlin Heidelberg 2004.