Memetic algorithms: a short introduction
New ideas in optimization
A note on low autocorrelation binary sequences
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
Evolutionary search for low autocorrelated binary sequences
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Engineering Stochastic Local Search for the Low Autocorrelation Binary Sequence Problem
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Finding low autocorrelation binary sequences with memetic algorithms
Applied Soft Computing
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Finding binary sequences with low auto correlation is a very hard problem with many practical applications. In this paper we analyze several meta heuristic 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 state-of-the-art algorithm: it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature.