Learning variable importance to guide recombination

In evolutionary multi-objective optimization, variation operators are crucially important to produce improving solutions, hence leading the search towards the most promising regions of the solution space. In this paper, we propose to use a machine learning modeling technique, namely random forest, i...

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Veröffentlicht in:	2016 IEEE Symposium Series on Computational Intelligence (SSCI) S. 1 - 7
Hauptverfasser:	Sagawa, Miyako, Aguirre, Hernan, Daolio, Fabio, Liefooghe, Arnaud, Derbel, Bilel, Verel, Sebastien, Tanaka, Kiyoshi
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.12.2016
Schlagworte:	Benchmark testing Convergence Pareto optimization Sociology Vegetation
Online-Zugang:	Volltext
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Zusammenfassung:	In evolutionary multi-objective optimization, variation operators are crucially important to produce improving solutions, hence leading the search towards the most promising regions of the solution space. In this paper, we propose to use a machine learning modeling technique, namely random forest, in order to estimate, at each iteration in the course of the search process, the importance of decision variables with respect to convergence to the Pareto front. Accordingly, we are able to propose an adaptive mechanism guiding the recombination step with the aim of stressing the convergence of the so-obtained offspring. By conducting an experimental analysis using some of the WFG and DTLZ benchmark test problems, we are able to elicit the behavior of the proposed approach, and to demonstrate the benefits of incorporating machine learning techniques in order to design new efficient adaptive variation mechanisms.
DOI:	10.1109/SSCI.2016.7850229