A diversity preservation scheme for DSMGA-II to conquer the hierarchical difficulty

Yu, Jheng-Ying; Chen, I-Ting; Yu, Tian-Li

doi:10.1145/3071178.3071253

Cited by 2 publications

(1 citation statement)

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“…The idea of the LTGA mentioned above has been applied to real-valued GAs [19]. Additionally, the DSMGA-II mentioned above has been kept improving since it was developed [20]. However, these model-based GAs tackle mainly with decomposable problems.…”

Section: Introductionmentioning

confidence: 99%

An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Ohnishi¹,

Hamano²,

Koeppen³

2020

Electronics

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Recently, evolutionary algorithms that can efficiently solve decomposable binary optimization problems have been developed. They are so-called model-based evolutionary algorithms, which build a model for generating solution candidates by applying a machine learning technique to a population. Their central procedure is linkage detection that reveals a problem structure, that is, how the entire problem consists of sub-problems. However, the model-based evolutionary algorithms have been shown to be ineffective for problems that do not have relevant structures or those whose structures are hard to identify. Therefore, evolutionary algorithms that can solve both types of problems quickly, reliably, and accurately are required. The objective of the paper is to investigate whether the evolutionary algorithm evolving developmental timings (EDT) that we previously proposed can be the desired one. The EDT makes some variables values more quickly converge than the remains for any problems, and then, decides values of the remains to obtain a higher fitness value under the fixation of the variables values. In addition, factors to decide which variable values converge more quickly, that is, developmental timings are evolution targets. Simulation results reveal that the EDT has worse performance than the linkage tree genetic algorithm (LTGA), which is one of the state-of-the-art model-based evolutionary algorithms, for decomposable problems and also that the difference in the performance between them becomes smaller for problems with overlaps among linkages and also that the EDT has better performance than the LTGA for problems whose structures are hard to identify. Those results suggest that an appropriate search strategy is different between decomposable problems and those hard to decompose.

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Section: Introductionmentioning

confidence: 99%