Abstract: To overcome global situation problem tradition genetic algorithm has very strong robustness in finding the solution，but crossover probability and mutation probability is fixed and invariable，it caused premature convergence and running inefficient to the solution on complicated problem at later evolution process of tradition genetic algorithm.To this problem，an adaptive genetic algorithm is proposed with varying population size based on lifetimes of the chromosomes to realize population size adjust adaptively and crossover probability adjust adaptively and mutation probability adjust adaptively.Experimental results show that the approach proposed is effective in the capability of global optimization and significantly improves the convergence rate.

Key words: adaptive genetic algorithm, premature convergence, crossover probability, mutation probability

摘要： 传统遗传算法在求解全局问题具有很强的鲁棒性，但由于传统遗传算法固定的交叉率和变异率，使得传统遗传算法在求解复杂问题上存在早收敛及搜索后期运行效率低等缺点。针对此问题，提出了基于个体寿命的变种群自适应遗传算法，对种群规模，交叉率及变异率作了优化调整，使其能够根据进化的实际情况自动调整。实验结果表明，相比传统遗传算法，这个算法在全局优化能力及收敛速度上均有显著提高。

关键词: 自适应遗传算法, 早收敛, 交叉率, 变异率