Authors: Marius Olteanu; Nicolae Paraschiv
Addresses: Department of Automatic Control, Computers and Electronics, Petroleum-Gas University, Ploiesti, Romania ' Department of Automatic Control, Computers and Electronics, Petroleum-Gas University, Ploiesti, Romania
Abstract: Genetic algorithms represent a technique of artificial intelligence which has developed from the paradigm of biological evolution. They use a population of potential solutions which gradually evolve toward the best solution which satisfies an objective function. By their nature, genetic algorithms use random numbers. In a typical algorithm running, a random number generator is used in many occasions, like selection of the best individuals, choosing the parents for crossover and actually applying crossover, and in mutation. Relying on a standard algorithm for random numbers has the advantage of simplicity and easy implementation (for example in embedded applications), but the quality of the random numbers could influence the final results. In this paper we investigate the effect of the random number generator used by a genetic algorithm in finding the optimal solution for two test functions.
Keywords: genetic algorithms; random numbers; random number generators.
International Journal of Reasoning-based Intelligent Systems, 2013 Vol.5 No.4, pp.274 - 279
Available online: 18 Jan 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article