Memetic algorithm (MA) is a kind of evolutionary algorithms (EAs) that searches the problem solving space using local search and global search. The balance between global search and local search is one of the key issues in this algorithm. In this paper a new model is pr More
Memetic algorithm (MA) is a kind of evolutionary algorithms (EAs) that searches the problem solving space using local search and global search. The balance between global search and local search is one of the key issues in this algorithm. In this paper a new model is proposed, called GALA2. This model is combined of genetic algorithm (GA) and object migration automata (OMA), which is a kind of fixed-structure learning automaton. In the proposed model, global search is performed by genetic algorithm and local learning is performed by learning automata. In this model, the Lamarckian and Baldwinian learning models have been used to increase the convergence rate and avoidance of premature convergence, simultaneously. In this evolutionary model, chromosomes are represented by object migration automata for the purpose of using positive effects of evolution and local learning. In order to show the superiority of the proposed model, GALA2 is used to solve the graph isomorphism problem.
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