The Hybridation of the Selfish Gene Algorithm

This paper proposes an improvement of a new general approach for optimization algorithms in the Evolutionary Computation field. The approach is inspired by the Selfish Gene Theory, an interpretation of the Darwinian theory given by the biologist Richard Dawkins, in which the basic element of evolution is the gene, rather than the individual. The paper analyses the performances of this algorithm, and proposes a method of improvement of these performances by hybridation with a Simulated Annealing technique. We tested the approach by implementing a Hybridated Selfish Gene Algorithm on a case study, and we found better results than those provided by the Selfish Gene Algorithm, on the same problem and with the same fitness function.