Warin Wattanapornprom
ABSTRACT
This article compares two edge-based Estimation of Distribution Algorithms named Edge Histogram Based Sampling Algorithm (EHBSA) and Coincidence Algorithm (COIN) in multimodal combinatorial puzzles benchmarks. Both EHBSA and COIN make use of joint probability matrix of adjacent events (edge) derived from the population of candidate solutions. These algorithms are expected to be competitive in solving problems where relative relation between two nodes is significant. The experiment results imply that EHBSAs are better in convergence to a single optima point, while COINs are better in maintaining the diversity among the population and are better in preventing the premature convergence.
- Tsutsui S., (2002) Probabilistic Model-Building Genetic Algorithms in Permutation Representation Domain Using Edge Histogram, (PPSN VII), pp. 224--233. Google Scholar
Digital Library
- Tsutsui S., (2006) Node Histogram vs. Edge Histogram: A Comparison of Probalistic Model-Building Genetic Algorithms in Permutation Domains, (CEC 2006).Google Scholar
- Wattanapornprom W. and Chongstitvatana P. (2009) Multiobjective Combinatorial Optimization with Coincidence Algorithm (CEC 2009). Google ScholarDigital Library
- Sirovetnukul R.and Chutima P. The Impact of Walking Time on U-shaped Assembly Line Worker Allocation Problems, Chulalongkorn University's Engineering Journal, Vol 14 issue 2 Apr. 2010Google Scholar
Index Terms
- Solving multimodal combinatorial puzzles with edge-based estimation of distribution algorithm
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