摘要翻译:
本文重点研究了CMA-ES在多模态函数上的重启策略。第一替代策略通过减小突变的初始步长而在每次重启时使种群规模加倍来进行。第二策略在BIPOP方案中的重新启动设置之间自适应地分配计算预算。两种重启策略都在BBOB基准上得到了验证;在一个与航天器轨迹优化相关的独立的现实问题集上,也证明了它们的一般性。
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英文标题:
《Alternative Restart Strategies for CMA-ES》
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作者:
Ilya Loshchilov (INRIA Saclay - Ile de France), Marc Schoenauer (INRIA
Saclay - Ile de France, MSR - INRIA), Mich\`ele Sebag (INRIA Saclay - Ile de
France, LRI)
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最新提交年份:
2012
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要:
This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization.
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PDF链接:
https://arxiv.org/pdf/1207.0206