A novel evolutionary algorithm for function optimization
一种新颖的函数优化进化算法

When an evolutionary algorithm is applied to global optimization problems, it may be trapped around the local optima of the objective function and has a low convergence-rate. To solve these problems, a crossover operator is developed based on a descent-marking function. This operator finds descent directions based on the relation between the descent-marking function and the population. To improve the search ability of a non-uniform mutation operator in the late stage of evolution, an improved non-uniform mutation operator is designed for balancing the ability of global search and local exploration, which makes the algorithm able to avoid the premature convergence in the final stage of evolution. Combining all these techniques, we present a novel evolutionary algorithm. The presented algorithm is compared with 9 existing ones by simulations. Finally, experimental results indicate that the proposed algorithm is fast and efficient for all the test functions.