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Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Hybrid Multievolutionary System to Solve Function Optimization Problems

DOI: http://dx.doi.org/10.15439/2017F85

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 87–90 ()

Full text

Abstract. Evolutionary algorithms are optimization methods inspired by observation of natural evolution. They usually search for the optimal solution in large space areas. In Evolutionary Algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an Evolutionary Strategy designed to optimize the function of many variables. In this system, we combined the ability of the Genetic Algorithm to explore the search space and the ability of the Evolutionary Strategy to exploit the search space. Optimization performed by the Genetic Algorithm and the Evolutionary Strategy runs at the same time, so it is possible to perform parallel computations. The results of the experiments suggest that the proposed system can be an effective tool in solving complex optimization problems.

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