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Article

An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings

Department of Computer Science and Networks, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
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Electronics 2020, 9(11), 1866; https://doi.org/10.3390/electronics9111866
Received: 19 October 2020 / Revised: 29 October 2020 / Accepted: 2 November 2020 / Published: 6 November 2020
(This article belongs to the Special Issue Evolutionary Machine Learning for Nature-Inspired Problem Solving)
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. View Full-Text
Keywords: model-based evolutionary algorithm; operator-based evolutionary algorithm; decomposable problem; non-decomposable problem; developmental timing model-based evolutionary algorithm; operator-based evolutionary algorithm; decomposable problem; non-decomposable problem; developmental timing
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MDPI and ACS Style

Ohnishi, K.; Hamano, K.; Koeppen, M. An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings. Electronics 2020, 9, 1866. https://doi.org/10.3390/electronics9111866

AMA Style

Ohnishi K, Hamano K, Koeppen M. An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings. Electronics. 2020; 9(11):1866. https://doi.org/10.3390/electronics9111866

Chicago/Turabian Style

Ohnishi, Kei, Kouta Hamano, and Mario Koeppen. 2020. "An Empirical Investigation on Evolutionary Algorithm Evolving Developmental Timings" Electronics 9, no. 11: 1866. https://doi.org/10.3390/electronics9111866

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