Knowledge learning based evolutionary algorithm for unconstrained optimization problem

In this paper, we propose a new evolutionary algorithm called nearest neighbor evolutionary algorithm (NNE) to solve the unconstrained optimization problem. Specifically, NNE consists of two major steps: coarse nearest neighbor evolutionary and fine nearest neighbor evolutionary. The coarse nearest neighbor evolutionary step pays more attention to searching the optimal solutions in the global way, while the fine nearest neighbor evolutionary step focuses on searching the best solutions in the local way. NNE repeats two major steps until the terminate condition is reached. NNE not only adopts the elitist strategy and maintains the best individuals for the next generation, but also considers the knowledge obtained in the searching process. The experiments demonstrate that (1) NNE achieves good performance in most of numerical optimization problems; (2) NNE outperforms most of state-of-art evolutionary algorithms, such as traditional genetic algorithm (GA), the jumping gene genetic algorithm (JGGA).