Global optimization; evolutionary algorithms; heuristics; convergence; self-adaptation of algorithm

The paper deals with a class of evolutionary algorithms (EAs) for the global optimization. The special attention is paid to the controlled random search (CRS). A~generalization of the EA is proposed with several heuristics competing with each other when generating new trial points. The condition for asymptotic convergence of the algorithm are briefly discussed. Two instances of the EA with competing heuristics were implemented and the experimental results obtained on several test functions are presented.

The paper deals with a class of evolutionary algorithms (EAs) for the global optimization. The special attention is paid to the controlled random search (CRS). A~generalization of the EA is proposed with several heuristics competing with each other when generating new trial points. The condition for asymptotic convergence of the algorithm are briefly discussed. Two instances of the EA with competing heuristics were implemented and the experimental results obtained on several test functions are presented.