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GECCO
2007
Springer

A memetic algorithm for the low autocorrelation binary sequence problem

9 years 5 months ago
A memetic algorithm for the low autocorrelation binary sequence problem
Finding binary sequences with low autocorrelation is a very hard problem with many practical applications. In this paper we analyze several metaheuristic approaches to tackle the construction of this kind of sequences. We focus on two different local search strategies, steepest descent local search (SDLS) and tabu search (TS), and their use both as stand-alone techniques and embedded within a memetic algorithm (MA). Plain evolutionary algorithms are shown to perform worse than stand-alone local search strategies. However, a MA endowed with TS turns out to be a stateof-the-art algorithm: it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature. Categories and Subject Descriptors
José E. Gallardo, Carlos Cotta, Antonio J.
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where GECCO
Authors José E. Gallardo, Carlos Cotta, Antonio J. Fernández
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