An Ensemble of Cooperative Genetic Algorithms as an Intelligent Search Tool
Halina Kwaśnicka · International Journal of Computational Intelligence Research · 2007
Evolutionary algorithms become very popular due to their searching skill in a solution space. The problem arises when we try to adjust used genetic operators and parameters. In literature one can find various, often sophisticated new ge- netic operators, specific for the particular task. In the proposed method a user can use a number of cooperating and specialized genetic algorithms with simple genetic operators and presumed parameters, but an intelligent agent takes care of tuning the pa- rameters. The agent tunes parameters dynamically on the basis of observed results. We have defined a number of measures used bytheagentasinputsforafuzzycontrolsystem. Thesetoffuzzy rules can be defined using experts'knowledge. The main advantage of the proposed system is releasing of GAs usersfromonerousduty-thedeterminationofgeneticoperators and values of their parameters. It is usually a time consuming task requiring extensive experience. The proposed system is flexible enough to solve the problems which potential solution can be represented as a string of real values. The paper presents the initial studies as well as the final propo- sition: GAAgent, an ensemble of cooperating genetic algorithms controlled by a set of fuzzy rules. The exemplary results are presented and discussed.