global optimization; evolutionary algorithms; competing heuristics; self-adaptation of algorithm

The aim of this paper is to study the properties of evolutionary algorithms with competing heuristics in dependence on several factors like the number of heuristics in competition, the values of their tuning parameters, the rules of competition and so on. Thirty six heuristics were implemented and their role in the evolutionary process was investigated. The optimization experiments were done with several well-known test functions (DeJong 1 and 2, Ackley, Griewangk). The main variables measured in experiments were the number of the objective function evaluations needed for reaching the stopping condition and the reliability of finding a point very near to the global minimum. The evolutionary algorithms with competing heuristics were more efficient than other evolutionary algorithms in some tasks.

The aim of this paper is to study the properties of evolutionary algorithms with competing heuristics in dependence on several factors like the number of heuristics in competition, the values of their tuning parameters, the rules of competition and so on. Thirty six heuristics were implemented and their role in the evolutionary process was investigated. The optimization experiments were done with several well-known test functions (DeJong 1 and 2, Ackley, Griewangk). The main variables measured in experiments were the number of the objective function evaluations needed for reaching the stopping condition and the reliability of finding a point very near to the global minimum. The evolutionary algorithms with competing heuristics were more efficient than other evolutionary algorithms in some tasks.