Knowledge evolution algorithm for solving unconstraint optimization problems and its convergence analysis
求解无约束优化问题的知识进化算法及其收敛性分析

To deal with the limitations in traditional algorithms, such as the random blindness and the traps of the local optima, we develop a knowledge evolution algorithm for solving unconstraint optimization problems(called UOP-KEA), and analyze its global convergence. Firstly, an initial knowledge base is formed; next, excellent knowledge individuals are inherited by inheritance operator; new knowledge individuals are produced by innovation operator; knowledge base is updated by update operator. Thus, knowledge evolution is realized. Finally, the optimal solution of issues is obtained from the optimal knowledge individuals. Experiments have been performed on optimization of unconstraint nonlinear test functions. Compared with genetic algorithms, this algorithm finds the global optimal solution with smaller size of population and in a higher speed. The successful results show that this algorithm is feasible and valid.