{"title":"Genetic algorithm for optimal multivariate mixture","authors":"Giacinto Angelo Sgarro, L. Grilli","doi":"10.12988/ams.2023.917307","DOIUrl":null,"url":null,"abstract":"This paper proposes an algorithm to find an optimal mixture that is as close as possible to an ideal solution, starting from a set of elements (items) described by a set of variables (features). This class of optimization problems can be tackled through traditional approaches belonging to the field of operations research (OR) or even through meta-heuristics techniques belonging to the field of artificial intelligence (AI). In order to present an artificial intelligence perspective, this paper uses a genetic algorithm (GA) model which proves its consistency through the comparison with a linear programming (LP) solver on a set of 8-items 5-features experiments. Results show that the proposed GA converges towards the global optimum and provides competitive results","PeriodicalId":49860,"journal":{"name":"Mathematical Models & Methods in Applied Sciences","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Models & Methods in Applied Sciences","FirstCategoryId":"100","ListUrlMain":"https://doi.org/10.12988/ams.2023.917307","RegionNum":1,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
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Abstract
This paper proposes an algorithm to find an optimal mixture that is as close as possible to an ideal solution, starting from a set of elements (items) described by a set of variables (features). This class of optimization problems can be tackled through traditional approaches belonging to the field of operations research (OR) or even through meta-heuristics techniques belonging to the field of artificial intelligence (AI). In order to present an artificial intelligence perspective, this paper uses a genetic algorithm (GA) model which proves its consistency through the comparison with a linear programming (LP) solver on a set of 8-items 5-features experiments. Results show that the proposed GA converges towards the global optimum and provides competitive results
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