Genetic Algorithm (GA), based on metaphors from the natural evolutionary process, is a famous random heuristic approach for solving complex optimization problems. However, the traditional GA is always subjected to the low convergence velocity and deceptions of multiple local optima. To overcome such inconvenience, a novel GA is proposed which entitled self-adaptive genetic algorithms (SaGA) in this paper. During the execution of the search process, the whole populations are classified into subgroups by sufficiently analyzed the individuals' state. Each individual in a different subset is assigned to the appropriate attribute (probabilities of crossover and mutation, pc, pm). Self-adaptive update the subgroups and adjust the control parameters, which are considered to be an optimal balance between exploration and exploitation. The empirical values and negative feedback technique are also used in parameters selection, which relieve the burden of specifying the parameters values. The new method is tested on a set of well-known benchmark test functions, and the simulation results suggest that it outperforms to other state-of-the-art techniques referred to in this paper in terms of the quality of the final solutions.
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机译:基于自然进化过程中的隐喻的遗传算法(GA)是解决复杂优化问题的著名随机启发式方法。但是,传统遗传算法总是收敛速度较慢,并且容易受到多个局部最优解的欺骗。为了克服这种不便,本文提出了一种新的遗传算法,其名称为自适应遗传算法(SaGA)。在搜索过程的执行过程中,通过充分分析个体的状态,将整个人口分为亚组。将不同子集中的每个人分配给适当的属性(交叉和突变的概率,p c inf>,p m inf>)。自适应更新子组并调整控制参数,这被认为是勘探与开发之间的最佳平衡。在选择参数时还使用了经验值和负反馈技术,这减轻了指定参数值的负担。该新方法已在一组著名的基准测试功能上进行了测试,仿真结果表明,就最终解决方案的质量而言,该方法优于本文中提到的其他最新技术。
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