Quantum-inspired evolutionary algorithm: a multimodel EDA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
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The contribution of the paper is bringing ensemble method to the field of evolutionary computation. The conceptive model of evolutionary algorithm ensemble is introduced, in which a collection of evolutionary algorithms are designed to solve the same problem and each interact with others. Two implementation methods are invented: data-based ensemble and model-based ensemble. In data-based ensemble, componential evolutionary algorithm shares a common data pool with others, and population of each algorithm is sampled from the pool using bagging method. In model-based ensemble, there are a collection of models describing the evolution status, and they cooperate by the way of information interaction. As examples, simple genetic algorithm and PBIL (population based incremental learning) are used to implement the ideas respectively. Experiments on combinatorial optimization problems show that ensemble method improves the performance of evolutionary algorithm. It can be concluded ensemble method can convert a 'weak' evolutionary algorithm to a 'strong' one.