{"title":"New coevolution dynamic as an optimization strategy in group problem solving","authors":"Francis Ferreira Franco, Paulo Freitas Gomes","doi":"10.1140/epjb/s10051-024-00828-8","DOIUrl":null,"url":null,"abstract":"<p>Coevolution on social models couples the time evolution of the network with the time evolution of the states of the agents. This paper presents a new coevolution dynamic allowing more than one rewiring on the network. We explore how this coevolution can be employed as an optimization strategy for problem-solving capability of task-forces. We use an agent-based model to study how this new coevolution dynamic can help a group of agents whose task is to find the global maxima of NK fitness landscapes. Each agent can replace more than one neighbor, and this quantity is a tunable parameter in the model. These rewirings are a way for the agent to obtain information from individuals that were not previously part of its neighborhood. Our results showed that this tunable coevolution can indeed produce gain on the computational cost under certain circumstances. At high average degree network and difficult landscape, the effect is complex. If the agent has a low fitness, 3 or 4 rewirings can bring some improvement.</p>","PeriodicalId":787,"journal":{"name":"The European Physical Journal B","volume":"97 12","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"The European Physical Journal B","FirstCategoryId":"4","ListUrlMain":"https://link.springer.com/article/10.1140/epjb/s10051-024-00828-8","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"PHYSICS, CONDENSED MATTER","Score":null,"Total":0}
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Abstract
Coevolution on social models couples the time evolution of the network with the time evolution of the states of the agents. This paper presents a new coevolution dynamic allowing more than one rewiring on the network. We explore how this coevolution can be employed as an optimization strategy for problem-solving capability of task-forces. We use an agent-based model to study how this new coevolution dynamic can help a group of agents whose task is to find the global maxima of NK fitness landscapes. Each agent can replace more than one neighbor, and this quantity is a tunable parameter in the model. These rewirings are a way for the agent to obtain information from individuals that were not previously part of its neighborhood. Our results showed that this tunable coevolution can indeed produce gain on the computational cost under certain circumstances. At high average degree network and difficult landscape, the effect is complex. If the agent has a low fitness, 3 or 4 rewirings can bring some improvement.
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