FRANÇAIS
A showcase of ÉTS researchers’ publications and other contributions
SEARCH

Bayesian network as an adaptive parameter setting approach for genetic algorithms

Downloads

Downloads per month over past year

Corriveau, Guillaume and Guilbault, Raynald and Tahan, Antoine and Sabourin, Robert. 2016. « Bayesian network as an adaptive parameter setting approach for genetic algorithms ». Complex & Intelligent Systems, vol. 2, nº 1. pp. 1-22.

[img]
Preview
PDF
Bayesian-network-as-an-adaptive-parameter-setting-approach-for-genetic-algorithms.pdf
Use licence: Creative Commons CC BY.

Download (6MB) | Preview

Abstract

Parameter setting currently ranks among the most actively researched topics in the evolutionary algorithm (EA) community. This can be explained by the major impact EA parameters have on search performance. However, parameter setting has been shown to be both problem dependent and evolution dependent.Moreover, because parameters interact in complex ways, developing an efficient and beneficial parameter setting approach is not an easy feat, and no broadly recognized solution has emerged to date. In this paper, we borrow the notion of parameter adaptationwith the objective of addressing the parameter setting dependencies mentioned above, using a strategy based on a Bayesian network. The adaptive framework is elaborated for a steady-state genetic algorithm (SSGA) to control nine parameters. To judge parameter state productivities, we consider the population’s fitness improvement, as well as exploration/exploitation balance management. The performance of this proposal, a Bayesian network for genetic algorithm parameter adaptation (BNGA), is assessed based on the CEC’05 benchmark. BNGA is compared to a static parameter setting, a naïve approach, three common adaptive systems (PM, AP, and FAUC–RMAB), and two state-of-theart EAs (CMA-ES and G-CMA-ES). Our results statistically demonstrate that the performance of BNGA is equivalent to that of FAUC–RMAB, CMA-ES, and G-CMA-ES, and overall is superior to that of all the other SSGA parameter setting approaches. However, these results also reveal that all the approaches considered have great difficulty finding global optima in a multimodal problem set. This suggests a lack of complementarity and/or synergy among parameter states.

Item Type: Peer reviewed article published in a journal
Professor:
Professor
Guilbault, Raynald
Tahan, Antoine
Sabourin, Robert
Affiliation: Génie mécanique, Génie mécanique, Génie de la production automatisée
Date Deposited: 14 Jul 2016 20:47
Last Modified: 24 Aug 2018 19:50
URI: http://espace2.etsmtl.ca/id/eprint/13287

Actions (login required)

View Item View Item