Enhancing covariance matrix adaptation evolution strategy through fitness inheritance

Evolution strategy (ES) has shown to be effective in many search and optimization problems. In particular, the ES with covariance matrix adaptation (CMAES) achieves great successes and is viewed as a state-of-the-art evolutionary algorithm for complex numerical optimization. The CMAES models the population by a multivariate normal distribution, which requires a considerable amount of fitness evaluation results and thus degrades its efficiency. This paper proposes using fitness inheritance to reduce the computational cost at fitness evaluation. More specifically, the proposed FI-CMAES adopts fitness inheritance to approximate the fitness of offspring. The survivors are selected according to the approximated fitness; thereafter, the survival offspring are evaluated by the original fitness function. By this way, several original fitness evaluations on offspring can be saved. Experiments examine the effectiveness and efficiency of FI-CMAES on the CEC2014 test suite. The results show that FI-CMAES can outperform CMAES in terms of solution quality and convergence speed.