Memetic algorithms: a short introduction
New ideas in optimization
Tabu Search
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
A memetic algorithm for the low autocorrelation binary sequence problem
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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
A new search for skewsymmetric binary sequences with optimal merit factors
IEEE Transactions on Information Theory
Sieves for low autocorrelation binary sequences
IEEE Transactions on Information Theory
The merit factor of long low autocorrelation binary sequences (Corresp.)
IEEE Transactions on Information Theory
GRASP for low autocorrelated binary sequences
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
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This paper deals with the construction of binary sequences with low autocorrelation, a very hard problem with many practical applications. The paper analyzes several metaheuristic approaches to tackle this kind of sequences. More specifically, the paper provides an analysis of different local search strategies, used as stand-alone techniques and embedded within memetic algorithms. One of our proposals, namely a memetic algorithm endowed with a Tabu Search local searcher, performs at the state-of-the-art, as it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature. Moreover, this algorithm is also able to provide new best-known solutions for large instances of the problem. In addition, a variant of this algorithm that explores only a promising subset of the whole search space (known as skew-symmetric sequences) is also analyzed. Experimental results show that this new algorithm provides new best-known solutions for very large instances of the problem.