A genetic algorithm with disruptive selection

Genetic algorithms are a class of adaptive search techniques based on the principles of population genetics. The metaphor underlying genetic algorithms is that of natural evolution. Applying the ''survival-of-the-fittest'' principle, traditional genetic algorithms allocate more trials to above-average schemata, However, increasing the sampling rate of schemata that are above average does not guarantee convergence to a global optimum; the global optimum could be a relatively isolated peak or located in schemata that have large variance in performance, In this paper we propose a novel selection method, disruptive selection. This method adopts a nonmonotonic fitness function that is quite different from traditional monotonic fitness functions, Unlike traditional genetic algorithms, this method favors both superior and inferior individuals. Experimental results show that GA's using the proposed method easily find the optimal solution of a function that is hard for traditional GA's to optimize, We also present convergence analysis to estimate the occurrence ratio of the optima of a deceptive function after a certain number of generations of a genetic algorithm, Experimental results show that GA's using disruptive selection in some occasions find the optima more quickly and reliably than GA's using directional selection, These results suggest that disruptive selection can be useful in solving problems that have large variance within schemata and problems that are GA-deceptive.