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

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: José E. Gallardo, Carlos Cotta, Antonio J. Fernández
• Keywords: Descent Local Search, GECCO 2007, Local Search Strategies, Stand-alone Local Search