Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State
We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/12/744 |
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| author | Itai Tzruia Tomer Halperin Moshe Sipper Achiya Elyasaf |
| author_facet | Itai Tzruia Tomer Halperin Moshe Sipper Achiya Elyasaf |
| author_sort | Itai Tzruia |
| collection | DOAJ |
| description | We present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population, and (3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than those of the fully run GA—depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators—where fitness computation is costly—our approach is generic and can be easily applied to many different domains. |
| format | Article |
| id | doaj-art-e53931c3daaf4dd6929d114353edb711 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-e53931c3daaf4dd6929d114353edb7112024-12-27T14:30:42ZengMDPI AGInformation2078-24892024-11-01151274410.3390/info15120744Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary StateItai Tzruia0Tomer Halperin1Moshe Sipper2Achiya Elyasaf3Department of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, IsraelDepartment of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, IsraelDepartment of Computer Science, Ben-Gurion University, Beer-Sheva 8410501, IsraelDepartment of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, IsraelWe present a novel approach to performing fitness approximation in genetic algorithms (GAs) using machine learning (ML) models, focusing on dynamic adaptation to the evolutionary state. We compare different methods for (1) switching between actual and approximate fitness, (2) sampling the population, and (3) weighting the samples. Experimental findings demonstrate significant improvement in evolutionary runtimes, with fitness scores that are either identical or slightly lower than those of the fully run GA—depending on the ratio of approximate-to-actual-fitness computation. Although we focus on evolutionary agents in Gymnasium (game) simulators—where fitness computation is costly—our approach is generic and can be easily applied to many different domains.https://www.mdpi.com/2078-2489/15/12/744genetic algorithmmachine learningfitness approximationsurrogate-assisted evolutionary algorithmregressionagent simulation |
| spellingShingle | Itai Tzruia Tomer Halperin Moshe Sipper Achiya Elyasaf Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State Information genetic algorithm machine learning fitness approximation surrogate-assisted evolutionary algorithm regression agent simulation |
| title | Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State |
| title_full | Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State |
| title_fullStr | Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State |
| title_full_unstemmed | Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State |
| title_short | Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary State |
| title_sort | fitness approximation through machine learning with dynamic adaptation to the evolutionary state |
| topic | genetic algorithm machine learning fitness approximation surrogate-assisted evolutionary algorithm regression agent simulation |
| url | https://www.mdpi.com/2078-2489/15/12/744 |
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