论文信息 - Evaluating alternative forms of crossover in evolutionary computation on linear systems of equations

Evaluating alternative forms of crossover in evolutionary computation on linear systems of equations

Experiments are conducted to assess the utility of alternative crossover operators within a framework of evolutionary computation. Systems of linear equations are used for testing the efficiency of one-point, two-point, and uniform crossover. The results indicate that uniform crossover, which disrupts building blocks maximally, generates statistically significantly better solutions than one- or two-point crossover. Moreover, for the cases of small population sizes, crossing over existing solutions with completely random solutions can perform as well or better than the traditional one- and two-point operators.

David B. Fogel | Peter J. Angeline

[1] Günter Rudolph,et al. Reflections on Bandit Problems and Selection Methods in Uncertain Environments , 1998, ICGA.

[2] Terry Jones,et al. Crossover, Macromutationand, and Population-Based Search , 1995, ICGA.

[3] D. Wolpert,et al. On 2-Armed Gaussian Bandits and Optimization , 1996 .

[4] David B. Fogel,et al. A note on representations and variation operators , 1997, IEEE Trans. Evol. Comput..

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

[6] David H. Wolpert,et al. No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[7] J. W. Atmar,et al. Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems , 1990, Biological Cybernetics.