Function optimization using evolutionary programming with self-adaptive cultural algorithms
Self-adaptation can take place at several levels in evolutionary computation system. Here we investigate relative performance of two different self-adaptive versions of Evolutionary Programming(EP). One at the individual level adaptation proposed by Schwefel and Saravanan & Fogel and one at the population level using Cultural Algorithms. The performance of the two versions of self-adaptive EP are then compared to each other for a set of selected unconstrained function optimization problems. For most optimization problems studied here, the pooling of information in the belief space at the population level improves the performance of EP.
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- Robert G. Reynolds, ChanJin Chung, A Self-adaptive Approach to Representation Shifts in Cultural Algorithms, in Proceedings of IEEE International Conference on Evolutionary Computation(ICEC'96), Nagoya, Japan 1996, pp. 94–99Google Scholar
- Peter A. Angeline, Adaptive and Self-Adaptive Evolutionary Computation, in Computation Intelligence, Eds. Marimuthu Palaniswami, et. Al., IEEE Press, New York, 1995, pp. 152–163.Google Scholar
- Robert G. Reynolds, An Introduction to Cultural Algorithms, in Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp. 131–139, 1994Google Scholar
- N. Saravanan and D. B. Fogel, Learning Strategy Parameters in Evolutionary Programming: An Empirical Study, in Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp.269–280, 1994Google Scholar
- J-H Kim, JY Jeon, HK Chae, KI Koh, A novel Evolutionary Algorithm with Fast Convergence, in Proceedings of IEEE International Conference on Evolutionary Computation (ICEC'95), 1995, pp. 819–824Google Scholar
- H. Schwefel, Evolution and Optimum Seeking, John Wiley & Sons, Inc., 1995Google Scholar
- Xin Yao and Yong Liu, Fast Evolutionary Programming, in Proceedings of the Fifth Annual Conference on Evolutionary Programming, 1996Google Scholar
- Anold Toynbee, A Study of History, 1934–1966Google Scholar