Rule Based Classifier Generation Using Symbiotic Evolutionary Algorithm

Genetic Algorithms are vastly used in development of rule based classifier systems in data mining. In such tasks, the rule base is usually a set of If-Then rules and the rules are developed using an evolutionary trait. GA is usually a good solution for such tasks as it globally searches for good rule-sets without any prior bias or greedy force, but it is usually slow. This paper presents a novel algorithm for rule base generation based on natural process of symbiogenesis. The algorithm uses symbiotic combination operator instead of traditional sexual recombination operator of genetic algorithms. The new algorithm is compared with genetic algorithm on some globally used benchmarks and it is shown that it can either find better classification results from that of GA, or finds similar results with much less computation time.