An improved genetic algorithm with conditional genetic operators and its application to set-covering problem

The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an improved genetic algorithm in which crossover and mutation are performed conditionally instead of probability. Because there are no crossover rate and mutation rate to be selected, the proposed improved GA can be more easily applied to a problem than the conventional genetic algorithms. The proposed improved genetic algorithm is applied to solve the set-covering problem. Experimental studies show that the improved GA produces better results over the conventional one and other methods.

[1]  Dirk Van Gucht,et al.  The effects of population size, heuristic crossover and local improvement on a genetic algorithm for the traveling salesman problem , 1989 .

[2]  Randy L. Haupt,et al.  Practical Genetic Algorithms , 1998 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Lawrence J. Schmitt,et al.  Performance characteristics of alternative genetic algorithmic approaches to the traveling salesman problem using path representation: An empirical study , 1998, Eur. J. Oper. Res..

[5]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[6]  John E. Beasley,et al.  OR-Library: Distributing Test Problems by Electronic Mail , 1990 .

[7]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[9]  Shaw Voon Wong,et al.  Optimization of fuzzy rules design using genetic algorithm , 2000 .

[10]  Rong-Song He,et al.  Improving real-parameter genetic algorithm with simulated annealing for engineering problems , 2006, Adv. Eng. Softw..

[11]  Darrell Whitley,et al.  Genitor: a different genetic algorithm , 1988 .

[12]  Charles E. Taylor.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Complex Adaptive Systems.John H. Holland , 1994 .

[13]  L. Darrell Whitley,et al.  Test driving three 1995 genetic algorithms: New test functions and geometric matching , 1995, J. Heuristics.

[14]  Emile H. L. Aarts,et al.  Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.

[15]  Mauricio Solar,et al.  A parallel genetic algorithm to solve the set-covering problem , 2002, Comput. Oper. Res..

[16]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[17]  Leslie K. Norford,et al.  A design optimization tool based on a genetic algorithm , 2002 .

[18]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.