A novel evolutionary algorithm for function optimization using MEC

This paper proposes a novel evolutionary algorithm that integrates Mind Evolutionary Computation (MEC) and non-uniform mutation. The algorithm greatly extends MEC to explore the tradeoff between exploration and exploitation for optimizing multimodal functions. Similartaxis mechanism drives the proposed algorithm to locate multiple local optima, while non-uniform method locates the global area cooperatively Moreover the 1/5 rule is adopted to guide the search direction based on information obtained from feedback. The proposed algorithm is experimentally testified with a test suits containing six complex multimodal function optimization problems. All experiments demonstrate that the proposed algorithm is competitive with other evolutionary algorithms published to date in both convergence velocity and solution quality.