Genetic and Local Search Algorithms as Robust and Simple Optimization Tools
Genetic and Local Search Algorithms as Robust and Simple Optimization Tools
复制标题
遗传和局部搜索算法作为强大而简单的优化工具
DOI：
10.1007/978-1-4613-1361-8_5
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发表时间：
1996
期刊：
影响因子：
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通讯作者：
T. Ibaraki
中科院分区：
文献类型：
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作者：
M. Yagiura;T. Ibaraki
近来元启发法的一大吸引力在于其稳健性和简单性。为了研究这个方向，单机调度问题通过各种遗传算法（GA）和随机多起点局部搜索算法（MLS）来解决，使用邻居、突变和交叉的相当简单的定义。结果表明：（1）如果采用突变实现，GA的性能对交叉不敏感；（2）MLS的简单实现通常可以与GA竞争（甚至更好）；（3）MLS的GRASP类型修改在一定程度上提高了其性能；（4）如果允许更长的计算时间，GA与局部搜索相结合是相当有效的。
One of the attractive features of recent metaheuristics is in its robustness and simplicity. To investigate this direction, the single machine scheduling problem is solved by various genetic algorithms (GA) and random multi-start local search algorithms (MLS), using rather simple definitions of neighbors, mutations and crossovers. The results indicate that: (1) the performance of GA is not sensitive about crossovers if implemented with mutations, (2) simple implementation of MLS is usually competitive with (or even better than) GA, (3) GRASP type modification of MLS improves its performance to some extent, and (4) G A combined with local search is quite effective if longer computational time is allowed.