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Playing in continuous spaces: some analysis and extension of population-based incremental learning

9 years 6 months ago
Playing in continuous spaces: some analysis and extension of population-based incremental learning
- As an alternative to traditional Evolutionary Algorithms (EAs), Population-Based Incremental Learning (PBIL) maintains a probabilistic model of the best individual(s). Originally, PBIL was applied in binary search spaces. Recently, some work has been done to extend it to continuous spaces. In this paper, we review two such extensions of PBIL. An improved version of the PBIL based on Gaussian model is proposed that combines two main features: a new updating rule that takes into account all the individuals and their fitness values and a self-adaptive learning rate parameter. Furthermore, a new continuous PBIL employing a histogram probabilistic model is proposed. Some experiment results are presented that highlight the features of the new algorithms.
Bo Yuan, Marcus Gallagher
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2003
Where CEC
Authors Bo Yuan, Marcus Gallagher
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