Improving on excellence: an evolutionary approach

Authors:
J. P. Caldeira;F. Melicio;A. Rosa
Affiliations:
I.P.S., E.S.T., Setúbal, Portugal and LASEEB, ISR, IST, Lisboa, Portugal;I.S.E.L., Lisboa, Portugal and LASEEB, ISR, IST, Lisboa, Portugal;LASEEB, ISR, IST, Lisboa, Portugal
Venue:
EC'05 Proceedings of the 6th WSEAS international conference on Evolutionary computing
Year:
2005

Citing 4
Cited 0

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Abstract

In this paper, we present a new hybridization method that, under certain conditions, can be used to improve results obtained by the best existing algorithms for a particular problem. The proposed hybrid uses Evolutionary Algorithm (EA) with a population of algorithms, to simultaneously evolve problem solutions and individual algorithm parameters. As an example of this approach we describe the details of its application to the Job Shop Problem (JSP) and use an EA to enhance results obtained by one of the most successful algorithms for this problem - Nowicki and Smutnicki's "Taboo Search Algorithm with back jump" (TSAB) [12]. The new algorithm not only improved TSAB's results but also improved the best known results for several well known benchmark problems.