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 standalone 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.

Branch-and-Bound and memetic algorithms represent two very different approaches for tackling combinatorial optimization problems. These approaches are not incompatible however. In this paper, we consider a hybrid model that combines these two techniques. To be precise, it is based on the interleaved execution of both approaches. Since the requirements of time and memory in branch-and-bound techniques are generally conflicting, we have opted for carrying out a truncated exact search, namely, beam search. The resulting hybrid algorithm has therefore a heuristic nature. The multidimensional 0-1 knapsack problem and the shortest common supersequence problem have been chosen as benchmarks. As will be shown, the hybrid algorithm can produce better results in both problems at the same computational cost, specially for large problem instances.

The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP. The paper describes PBS and provides an experimental analysis (including comparisons with previous approaches) that demonstrate its usefulness.

An important branch of hybrid metaheuristics concerns the hybridization with branch & bound derivatives. In this chapter we present examples for two different types of hybridization. The first one concerns the use of branch & bound features within construction-based metaheuristics in order to increase their efficiancy. The second example deals with the use of a metaheuristic, in our case a memetic algorithm, in order to increase the efficiancy of branch & bound, respectively branch & bound derivatives such as beam search. The quality of the resulting hybrid techniques is demonstrated by means of the application to classical string problems: the longest common subsequence problem and the shortest common supersequence problem.

Abstract.A hybridization of an evolutionary algorithm (EA) with the branch and bound method (B&B) is presented in this paper. Both techniques cooperate by exchanging information, namely lower bounds in the case of the EA, and partial promising solutions in the case of the B&B. The multidimensional knapsack problem has been chosen as a benchmark. To be precise, the algorithms have been tested on large problems instances from the OR-library. As it will be shown, the hybrid approach can provide high quality results, better than those obtained by the EA and the B&B on their own.