论文信息 - Real royal road functions for constant population size

Real royal road functions for constant population size

Evolutionary and genetic algorithms (EAs and GAs) are quite successful randomized function optimizers. This success is mainly based on the interaction of different operators like selection, mutation, and crossover. Since this interaction is still not well understood, one is interested in the analysis of the single operators. Jansen and Wegener [Proceedings of GECCO'2001, 2001, pp. 375-382] have described so-called real royal road functions where simple steady-state GAs have a polynomial expected optimization time while the success probability of mutation-based EAs is exponentially small even after an exponential number of steps. This success of the GA is based on the crossover operator and a population whose size is moderately increasing with the dimension of the search space. Here new real royal road functions are presented where crossover leads to a small optimization time, although the GA works with the smallest possible population size--namely 2.

[1] Martin Dietzfelbinger,et al. The analysis of a recombinative hill-climber on H-IFF , 2003, IEEE Trans. Evol. Comput..

[2] Ingo Wegener,et al. On the utility of populations in evolutionary algorithms , 2001 .

[3] Rajeev Motwani,et al. Randomized Algorithms , 1995, SIGA.

[4] Ingo Wegener,et al. Real royal road functions--where crossover provably is essential , 2001, Discret. Appl. Math..

[5] Dorothea Heiss-Czedik,et al. An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[6] John H. Holland,et al. When will a Genetic Algorithm Outperform Hill Climbing , 1993, NIPS.

[7] Melanie Mitchell,et al. The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .

[8] Thomas Jansen,et al. Design and Management of Complex Technical Processes and Systems by means of Computational Intelligence Methods Evolutionary Algorithms-How to Cope With Plateaus of Constant Fitness and When to Reject Strings of the Same Fitness , 2001 .

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

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

[11] J. Pollack,et al. Hierarchically consistent test problems for genetic algorithms , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).