Genetic algorithms with adaptively conserving species seeds for function optimization
用于函数优化的自适应类种子保留遗传算法

Genetic algorithms with conserving species seeds can cope with the conflict between maintaining the diversity of the evolutionary population and conserving the important individuals,but there is no effective way to determine their species control parameter.A strategy for adaptively changing the species control parameter along with evolutionary phase is proposed.The methodology adopted is that the species control parameter is big in prophase so that the evolutionary population is divided into a small number of coarse species.Along with evolution,the species control parameter decreases adaptively so that the evolutionary population is divided into a large number of fine species.Besides,the adaptive mutation operator makes fully use of the information of the current state of being mutated individual,its own species seed and the best seed of the evolutionary population.The algorithms proposed are applied to 5 benchmark problems of numerical function optimization.It is validated from the computational results that the algorithms decrease the computational complexity on the premise of finding multi-optima of the problems being optimized and hence increasing the evolutionary efficiency.