Orthogonal Genetic Algorithm and its Application in Function Optimization

Abstract:

Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal test then applied it to the population initialization, crossover operator, as well as the introduction of adaptive orthogonal local search to prevent local convergence to form a new orthogonal evolutionary algorithm. Through the series of numerical experiments, proved the efficiency of the new algorithm.

• Periodical: Applied Mechanics and Materials (Volumes 121-126)
• Pages: 4528-4531
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.121-126.4528
• Online since: October 2011
• Authors: Han Min Liu, Guang Ming Dai, Xue Song Yan
• Keywords: Evolutionary Algorithm (EA), Local Search, Orthogonal Evolutionary Algorithm

References

[1] J. Holland, Adaptation in natural and artificial systems. University of Michigan press (1975).

[2] Leung Yiu-Wing, Wang Yuping. An orthogonal genetic algorithm with quantization for global numerical optimization. IEEE Transactions on Evolutionary Computation, 5(1)(2001), pp.41-53.

DOI: 10.1109/4235.910464