Evaluating a local genetic algorithm as context-independent local search operator for metaheuristics

Local genetic algorithms have been designed with the aim of providing effective intensification. One of their most outstanding features is that they may help classical local search-based metaheuristics to improve their behavior. This paper focuses on experimentally investigating the role of a recent approach, the binary-coded local genetic algorithm (BLGA), as context-independent local search operator for three local search-based metaheuristics: random multi-start local search, iterated local search, and variable neighborhood search. These general-purpose models treat the objective function as a black box, allowing the search process to be context-independent. The results show that BLGA may provide an effective and efficient intensification, not only allowing these three metaheuristics to be enhanced, but also predicting successful applications in other local search-based algorithms. In addition, the empirical results reported here reveal relevant insights on the behavior of classical local search methods when they are performed as context-independent optimizers in these three well-known metaheuristics.

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