A Self-adaptive Mutations with Multi-parent Crossover Evolutionary Algorithm for Solving Function Optimization Problems

摘要

In this paper, we introduce a new self-adaptive evolutionary algorithm for solving function optimization problems. The capabilities of the new algorithm include: a) self-adaptive choice of Gaussian or Cauchy mutation to balance the local and global search on the variable subspace, b) using multi-parent crossover to exchange global search information, c) using the best individual to take place the worst individual selection strategy to reduce the selection pressure and ensure to find a global optimization. These enhancements increase the capabilities of the algorithm to solve Shekel problems in a more robust and universal way. This paper will present some results of numerical experiments which show that the new algorithm is more robust and universal than its competitors.