Bo Yuan
ABSTRACT
The development of Estimation of Distribution Algorithms (EDAs) has largely been driven by using more and more complex statistical models to approximate the structure of search space. However, there are still problems that are difficult for EDAs even with models capable of capturing high order dependences. In this paper, we show that diversity maintenance plays an important role in the performance of EDAs. A continuous EDA based on the Cholesky decomposition is tested on some well-known difficult benchmark problems to demonstrate how different diversity maintenance approaches could be applied to substantially improve its performance.
- Baluja, S. and Davies, S. Using Optimal Dependency-Trees for Combinatorial Optimization: Learning the Structure of the Search Space. In Proceedings of the Fourteenth International Conference on Machine Learning, 1997, 30--38. Google Scholar
Digital Library
- Bosman, P.A.N. and Thierens, D. An Algorithmic Framework For Density Estimation Based Evolutionary Algorithms. Technical Report UU-CS-1999-46, Utrecht University, 1999.Google Scholar
- Bosman, P.A.N. and Thierens, D. Mixed IDEAs. Technical Report UU-CS-2000-45, Utrecht University, 2000.Google Scholar
- Cho, D.-Y. and Zhang, B.-T. Evolutionary Continuous Optimization by Distribution Estimation with Variational Bayesian Independent Component Analyzers Mixture Model. In Proceedings of Parallel Problem Solving from Nature VIII, 2004, 212--221.Google Scholar
- Kern, S., Müller, S.D., Hansen, N., Büche, D., Ocenasek, J. and Koumoutsakos, P. Learning Probability Distributions in Continuous Evolutionary Algorithms - A Comparative Review. Natural Computing, 3, 1 (2004), 72--112. Google ScholarDigital Library
- Kullback, S. and Leibler, R.A. On information and sufficiency. The Annals of Mathematical Statistics, 22, 1 (1951), 79--86.Google ScholarCross Ref
- Larrañaga, P., Etxeberria, R., Lozano, J.A. and Pena, J.M. Optimization by learning and simulation of Bayesian and Gaussian networks. Technical Report EHU-kZAA-IK-4/99, University of the Basque Country, 1999.Google Scholar
- Larrañaga, P. and Lozano, J.A. (eds.) Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, 2001. Google ScholarDigital Library
- Lu, Q. and Yao, X. Clustering and Learning Gaussian Distribution for Continuous Optimization. Accepted by IEEE Transactions on Systems, Man, and Cybernetics, Part C (2004) Google ScholarDigital Library
- Mahfoud, S.M. Crowding and Preselection Revisited. In Proceedings of Parallel Problem Solving From Nature II, 1992, 27--36.Google Scholar
- Mühlenbein, H. and Paaβ, G. From Recombination of Genes to the Estimation of Distributions: I. Binary Parameters. In Proceedings of Parallel Problem Solving from Nature IV, 1996, 178--187. Google ScholarDigital Library
- Nash, J.C. Compact numerical methods for computers: linear algebra and function minimisation. Adam Hilger, 1990.Google Scholar
- Paul, T.K. and Iba, H. Real-Coded Estimation of Distribution Algorithm. In Proceedings of The Fifth Metaheuristics International Conference, 2003.Google Scholar
- Ripley, B.D. Stochastic Simulation. John Wiley & Sons, 1987. Google ScholarDigital Library
- Schwefel, H.-P. Evolution and Optimum Seeking. Wiley, New York, 1995. Google ScholarDigital Library
Index Terms
- On the importance of diversity maintenance in estimation of distribution algorithms
Recommendations
Diversity Guided Evolutionary Programming: A novel approach for continuous optimization
Avoiding premature convergence to local optima and rapid convergence towards global optima has been the major concern with evolutionary systems research. In order to avoid premature convergence, sufficient amount of genetic diversity within the evolving ...
Real-valued evolutionary multi-modal optimization driven by hill-valley clustering
GECCO '18: Proceedings of the Genetic and Evolutionary Computation ConferenceModel-based evolutionary algorithms (EAs) adapt an underlying search model to features of the problem at hand, such as the linkage between problem variables. The performance of EAs often deteriorates as multiple modes in the fitness landscape are ...
Niching an estimation-of-distribution algorithm by hierarchical Gaussian mixture learning
GECCO '17: Proceedings of the Genetic and Evolutionary Computation ConferenceEstimation-of-Distribution Algorithms (EDAs) have been applied with quite some success when solving real-valued optimization problems, especially in the case of Black Box Optimization (BBO). Generally the performance of an EDA depends on the match ...
Comments