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) within a practical time. While those papers discuss techniques to escape from local optima, this paper describes a method that actively performs global searches. The present method improves the rate of search of genetic algorithms by using viral infection instead of mutation. 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. The search for a solution is conducted by crossover and infection. 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 that usual genetic algorithms at finding a solution when the constraint density of a CSP is low.

[1]  Bart Selman,et al.  Systematic Versus Stochastic Constraint Satisfaction , 1995, IJCAI.

[2]  Paul Morris,et al.  The Breakout Method for Escaping from Local Minima , 1993, AAAI.

[3]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[4]  Hitoshi Kanoh,et al.  Genetic algorithms for constraint satisfaction problems , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[5]  James Bowen,et al.  Solving Constraint Satisfaction Problems Using a Genetic/Systematic Search Hybrid That Realizes When to Quit , 1995, ICGA.

[6]  Steven Minton,et al.  Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems , 1992, Artif. Intell..

[7]  N. Anderson.  Evolutionary Significance of Virus Infection , 1970, Nature.

[8]  Hector J. Levesque,et al.  Hard and Easy Distributions of SAT Problems , 1992, AAAI.

[9]  Bart Selman,et al.  Domain-Independent Extensions to GSAT : Solving Large StructuredSatis ability , 1993 .

[10]  김인철,et al.  Consistent Labeling Problem을 풀기 위한 휴우리스틱 탐색 기법 , 1988 .

[11]  A. E. Eiben,et al.  Solving constraint satisfaction problems using genetic algorithms , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[12]  Mark D. Johnston,et al.  A discrete stochastic neural network algorithm for constraint satisfaction problems , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[13]  Andrew J. Davenport,et al.  GENET: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement , 1994, AAAI.