Comparing multimodal optimization and illumination
Comparing multimodal optimization and illumination
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比较多模态优化和照明
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Jean
中科院分区：
文献类型：
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作者：
Vassilis Vassiliades;Konstantinos Chatzilygeroudis;Jean
光照算法是进化计算工具箱的新成员，它允许在一次运行中生成许多不同的高性能解决方案。然而，传统的多模态优化算法也在寻找多样化和高性能的解决方案：是否有些多模态优化算法在照明方面比照明算法更好？在本研究中，我们比较了两种照明算法（新颖性搜索与局部竞争（NSLC），地图精英）和两种多模态优化算法（清理，限制比赛选择）在迷宫导航任务中的应用。结果表明，clearclear具有与MAP-Elites和NSLC相当的性能。
Illumination algorithms are a recent addition to the evolutionary computation toolbox that allows the generation of many diverse and high-performing solutions in a single run. Nevertheless, traditional multimodal optimization algorithms also search for diverse and high-performing solutions: could some multimodal optimization algorithms be better at illumination than illumination algorithms? In this study, we compare two illumination algorithms (Novelty Search with Local Competition (NSLC), MAP-Elites) with two multimodal optimization ones (Clearing, Restricted Tournament Selection) in a maze navigation task. The results show that Clearing can have comparable performance to MAP-Elites and NSLC.