Clonal Selection Algorithm with GEP Code for Function Modeling
Mo Hai · 2013
The clonal selection algorithm evolves through selecting best individuals,cloning the selected ones and hypermutation. The general method to find the best individuals is to sort the individuals according to their fitness. However,the GEP codes of those chromosomes with same fitness may be different. If duplicate individuals are allowed to appear in the sorted population,the duplicate superior individuals will be cloned excessively. In this case,the diversity of the population is decreased. If individuals are sorted just according to their fitness,the duplicate ones will be removed. And some best individuals with different codes may be abandoned. In order to maintain the diversity of population and increase the convergence rate,an improved clonal selection algorithm is proposed. Firstly,the individuals are sorted according to their fitness. Then,if there are multiple best individuals with same fitness,their codes are compared. The best individuals with different codes will be selected to clone. The experimental result shows that the proposed method maintains the diversity of population and increases the convergence rate.