A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation

Gladwin, D. and Stewart, P. and Stewart, J. (2011) A controlled migration genetic algorithm operator for hardware-in-the-loop experimentation. Engineering Applications of Artificial Intelligence, 24 (4). pp. 586-594. ISSN 0952-1976

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Full text URL: http://dx.doi.org/10.1016/j.engappai.2011.01.006

Abstract

In this paper, we describe the development of an extended migration operator, which combats the negative effects of noise on the effective search capabilities of genetic algorithms. The research is motivated by the need to minimize the num- ber of evaluations during hardware-in-the-loop experimentation, which can carry a significant cost penalty in terms of time or financial expense. The authors build on previous research, where convergence for search methods such as Simulated Annealing and Variable Neighbourhood search was accelerated by the implementation of an adaptive decision support operator. This methodology was found to be effective in searching noisy data surfaces. Providing that noise is not too significant, Genetic Al- gorithms can prove even more effective guiding experimentation. It will be shown that with the introduction of a Controlled Migration operator into the GA heuristic, data, which repre- sents a significant signal-to-noise ratio, can be searched with significant beneficial effects on the efficiency of hardware-in-the- loop experimentation, without a priori parameter tuning. The method is tested on an engine-in-the-loop experimental example, and shown to bring significant performance benefits.

Item Type:Article
Keywords:Genetic algorithms, Hardware-in-the-loop, Migration, Response surfaces, Engines
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Engineering
ID Code:3832
Deposited By:INVALID USER
Deposited On:13 Jan 2011 18:37
Last Modified:04 Dec 2013 21:35

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