Correlation-based genetic algorithm for real-parameter optimization

We propose a genetic algorithm (GA) by taking into account the correlation between the current best candidate with the other candidates in the population. In this paper we propose a new selection operator and in addition we have designed a prediction operator which works on an archive of selected candidates. We test our proposed algorithm on the problem definitions for the CEC 2014 special session and competition on single objective real-parameter numerical optimization