Solving constraint satisfaction problems by a genetic algorithm adopting viral infection

Abstract

Several approximate algorithms have been reported to solve large constraint satisfaction problems (CSPs) in a practical time. While these papers discuss techniques to escape from local optima, the present paper describes a method that actively performs global search. The present method is to improve the rate of search of genetic algorithms using viral infection instead of mutation. The partial solutions of a CSP are considered to be viruses and a population of viruses is created as well as a population of candidate solutions. Search for a solution is conducted by crossover infection substitutes the gene of a virus for the locus decided by the virus. Experimental results using randomly generated CSPs prove that the proposed method is faster than a usual genetic algorithm in finding a solution when the constraint density of a CSP is low.