Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Expanding from Discrete to Continuous Estimation of Distribution Algorithms: The IDEA
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The correlation-triggered adaptive variance scaling IDEA
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Unified eigen analysis on multivariate Gaussian based estimation of distribution algorithms
Information Sciences: an International Journal
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
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Simple continuous estimation of distribution algorithms are applied to a benchmark real-world set of problems: packing circles in a square. Although the algorithms tested are very simple and contain minimal parameters, it is found that performance varies surprisingly with parameter settings, specifically the population size. Furthermore, the population size that produced the best performance is an order of magnitude larger that the values typically used in the literature. The best results in the study improve on previous results with EDAs on this benchmark, but the main conclusion of the paper is that algorithm parameter settings need to be carefully considered when applying metaheuristic algorithms to different problems and when evaluating and comparing algorithm performance.