Mutation Strength Adaptation of the $(μ/μ_I, λ)$-ES for Large Population Sizes on the Sphere Function

Amir Omeradzic, Hans-Georg Beyer
{"title":"Mutation Strength Adaptation of the $(μ/μ_I, λ)$-ES for Large Population Sizes on the Sphere Function","authors":"Amir Omeradzic, Hans-Georg Beyer","doi":"arxiv-2408.09761","DOIUrl":null,"url":null,"abstract":"The mutation strength adaptation properties of a multi-recombinative\n$(\\mu/\\mu_I, \\lambda)$-ES are studied for isotropic mutations. To this end,\nstandard implementations of cumulative step-size adaptation (CSA) and mutative\nself-adaptation ($\\sigma$SA) are investigated experimentally and theoretically\nby assuming large population sizes ($\\mu$) in relation to the search space\ndimensionality ($N$). The adaptation is characterized in terms of the\nscale-invariant mutation strength on the sphere in relation to its maximum\nachievable value for positive progress. %The results show how the different\n$\\sigma$-adaptation variants behave as $\\mu$ and $N$ are varied. Standard\nCSA-variants show notably different adaptation properties and progress rates on\nthe sphere, becoming slower or faster as $\\mu$ or $N$ are varied. This is shown\nby investigating common choices for the cumulation and damping parameters.\nStandard $\\sigma$SA-variants (with default learning parameter settings) can\nachieve faster adaptation and larger progress rates compared to the CSA.\nHowever, it is shown how self-adaptation affects the progress rate levels\nnegatively. Furthermore, differences regarding the adaptation and stability of\n$\\sigma$SA with log-normal and normal mutation sampling are elaborated.","PeriodicalId":501347,"journal":{"name":"arXiv - CS - Neural and Evolutionary Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Neural and Evolutionary Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.09761","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The mutation strength adaptation properties of a multi-recombinative $(\mu/\mu_I, \lambda)$-ES are studied for isotropic mutations. To this end, standard implementations of cumulative step-size adaptation (CSA) and mutative self-adaptation ($\sigma$SA) are investigated experimentally and theoretically by assuming large population sizes ($\mu$) in relation to the search space dimensionality ($N$). The adaptation is characterized in terms of the scale-invariant mutation strength on the sphere in relation to its maximum achievable value for positive progress. %The results show how the different $\sigma$-adaptation variants behave as $\mu$ and $N$ are varied. Standard CSA-variants show notably different adaptation properties and progress rates on the sphere, becoming slower or faster as $\mu$ or $N$ are varied. This is shown by investigating common choices for the cumulation and damping parameters. Standard $\sigma$SA-variants (with default learning parameter settings) can achieve faster adaptation and larger progress rates compared to the CSA. However, it is shown how self-adaptation affects the progress rate levels negatively. Furthermore, differences regarding the adaptation and stability of $\sigma$SA with log-normal and normal mutation sampling are elaborated.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
球函数上大种群规模的$(μ/μ_I, λ)$-ES突变强度适应性研究
针对各向同性突变,研究了多重组$(\mu/\mu_I, \lambda)$-ES的突变强度适应特性。为此,实验和理论研究了累积步长适应(CSA)和突变自适应($\sigma$SA)的标准实现,假设种群规模($\mu$)与搜索间隔维度($N$)相关较大。适应性的特征是球体上的规模不变突变强度与正进展的最大可实现值的关系。结果显示了不同的$\sigma$适应变体在$\mu$和$N$变化时的表现。标准 CSA 变体在球面上显示出明显不同的适应特性和进展速度,随着 $\mu$ 或 $N$ 的变化而变慢或变快。与 CSA 相比,标准的 $\sigma$SA 变体(使用默认学习参数设置)可以获得更快的适应性和更大的进展率。此外,还阐述了采用对数正态和正态突变采样的 CSA 在适应性和稳定性方面的差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Hardware-Friendly Implementation of Physical Reservoir Computing with CMOS-based Time-domain Analog Spiking Neurons Self-Contrastive Forward-Forward Algorithm Bio-Inspired Mamba: Temporal Locality and Bioplausible Learning in Selective State Space Models PReLU: Yet Another Single-Layer Solution to the XOR Problem Inferno: An Extensible Framework for Spiking Neural Networks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1