The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses
The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses
复制标题
进化算法中种群多样性的好处：严格运行时分析的调查
DOI：
10.1007/978-3-030-29414-4_8
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Dirk Sudholt
中科院分区：
文献类型：
--
作者：
Dirk Sudholt
在进化算法中，种群多样性是实现全局探索和避免由于过早收敛而导致性能差的关键。本章回顾了通过明确的多样性机制或通过自然出现的多样性显示种群多样性益处的运行时分析。这些分析表明，多样性的好处是多方面的：多样性对全球勘探和找到几个全球最优的能力很重要。多样性增强了交叉，使交叉比变异更有效。当问题随着时间的推移而变化时，多样性在动态优化中可能是至关重要的。最后，为进化多目标优化中整个Pareto前沿的搜索提供了便利。
Population diversity is crucial in evolutionary algorithms to enable global exploration and to avoid poor performance due to premature convergence. This chapter reviews runtime analyses that have shown benefits of population diversity, either through explicit diversity mechanisms or through naturally emerging diversity. These analyses show that the benefits of diversity are manifold: diversity is important for global exploration and the ability to find several global optima. Diversity enhances crossover and enables crossover to be more effective than mutation. Diversity can be crucial in dynamic optimization, when the problem landscape changes over time. And, finally, it facilitates the search for the whole Pareto front in evolutionary multiobjective optimization.
DOI：
10.1145/1389095.1389202
发表时间：
2008-07
期刊：
Theor. Comput. Sci.
影响因子：
--
作者：
Benjamin Doerr;Edda Happ;Christian Klein
通讯作者：
Benjamin Doerr;Edda Happ;Christian Klein
影响因子：
14.3
作者：
Corus, Dogan;Oliveto, Pietro S.
通讯作者：
Oliveto, Pietro S.