Automatic configuration of metaheuristic algorithms for complex combinatorial optimization problems

We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial optimization problems. We term it automatic configuration engine for metaheuristics (ACEM). We first propose a novel left variation-right property (LVRP) tree structure to manage various metaheuristic procedures and properties. With LVRP tree, feasible configurations of metaheuristics can be easily specified. An evolutionary learning algorithm is then proposed to evolve the internal context of the trees based on pre-selected training set. Guided by a user-defined satisfaction function of the candidate algorithms, it converges to the optimal or a very good algorithm. The experimental comparison with two recent state-of-the-art algorithms for solving the quadratic assignment problem (QAP) shows that ACEM produces an hybrid-genetic algorithm with human-competitive or even better performance.