计算机科学
渡线
水准点(测量)
数学优化
航程(航空)
最优化问题
算法
遗传算法
元优化
操作员(生物学)
工程优化
优化算法
数学
人工智能
机器学习
生物化学
材料科学
化学
大地测量学
抑制因子
转录因子
复合材料
基因
地理
作者
Saber M. Elsayed,Ruhul A. Sarker,Daryl Essam
标识
DOI:10.1016/j.engappai.2013.09.013
摘要
Over the last two decades, many different genetic algorithms (GAs) have been introduced for solving optimization problems. Due to the variability of the characteristics in different optimization problems, none of these algorithms has shown consistent performance over a range of real world problems. The success of any GA depends on the design of its search operators, as well as their appropriate integration. In this paper, we propose a GA with a new multi-parent crossover. In addition, we propose a diversity operator to be used instead of mutation and also maintain an archive of good solutions. Although the purpose of the proposed algorithm is to cover a wider range of problems, it may not be the best algorithm for all types of problems. To judge the performance of the algorithm, we have solved aset of constrained optimization benchmark problems, as well as 14 well-known engineering optimization problems. The experimental analysis showed that the algorithm converges quickly to the optimal solution and thus exhibits a superior performance in comparison to other algorithms that also solved those problems.
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