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Improved Population-based incremental learning in Continuous spaces

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Abstract

Population-based incremental learning (PBIL) is one of the well-established Evolutionary algorithms (EAs). This method, although having outstanding Search performance, has been somewhat overlooked compared to other popular EAs. Since the first version of PBIL, which is based on binary search space, several Real code versions of PBIL have been introduced; nevertheless, they have been less popular than their binary code counterpart. In this paper, a Population-based incremental learning algorithm dealing with real Design variables is proposed. The method achieves optimization search with the use of a probability matrix, which is an extension of the Probability vector used in binary PBIL. Three variants of the new Real code PBIL are proposed while a comparative performance is conducted. The benchmark results show that the present PBIL algorithm outperforms both its binary versions and the previously developed continuous PBIL. The new methods are also compared with well-established and newly developed EAs and it is shown that the proposed real-code PBIL can rank among the high performance EAs. © Springer-Verlag Berlin Heidelberg 2011.

Population-based incremental learning (20 items found) | Evolutionary algorithms (206 items found) | Search performance (12 items found) | Probability vector (1 items found) | Design variables (9 items found) | Real code (2 items found) | EAs (3 items found) | Population-based incremental learningBinary search | Industrial applications | Performance comparison | Probability matrixes | Learning algorithms | Continuous domains | Continuous spaces | Continuous domain | Meta-heuristics | Pbil algorithms | Meta heuristics | Soft computing |

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