{"title":"球函数上大种群规模的$(μ/μ_I, λ)$-ES突变强度适应性研究","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":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"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\":\"3 1\",\"pages\":\"\"},\"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}","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}
Mutation Strength Adaptation of the $(μ/μ_I, λ)$-ES for Large Population Sizes on the Sphere Function
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.