Results 11 to 20 of about 2,841,488 (326)
Knowledge-Based Perturbation LaF-CMA-ES for Multimodal Optimization
Applied SciencesMultimodal optimization presents a significant challenge in optimization problems due to the existence of multiple attraction basins. Balancing exploration and exploitation is essential for the efficiency of algorithms designed to solve these problems ...
Huan Liu, Lijing Qin, Zhao Zhou
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Methods in Ecology and Evolution, 2023 Two main types of species distribution models are used to project species range shifts in future climatic conditions: correlative and process‐based models.
Victor Van der Meersch +1 more
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Hybrid Genetic Algorithm and CMA-ES Optimization for RNN-Based Chemical Compound Classification
MathematicsThe compound classification strategies addressed in this study encounter challenges related to either low efficiency or accuracy. Precise classification of chemical compounds from SMILES symbols holds significant importance in domains such as drug ...
Zhenkai Guo, Dianlong Hou, Qiang He
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Proceedings of the Genetic and Evolutionary Computation ConferenceIn several real-world applications in medical and control engineering, there are unsafe solutions whose evaluations involve inherent risk. This optimization setting is known as safe optimization and formulated as a specialized type of constrained optimization problem with constraints for safety functions. Safe optimization requires performing efficient
Kento Uchida +4 more
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CMA-ES with Learning Rate Adaptation: Can CMA-ES with Default Population Size Solve Multimodal and Noisy Problems? [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2023 Nominated for the best paper of GECCO'23 ENUM Track. We have corrected the error of Eq.(7)
Masahiro Nomura +2 more
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Proceedings of the Genetic and Evolutionary Computation Conference, 2022 Nominated for the best paper of GECCO'22 ENUM Track. We have corrected the error of Algorithm 1 in the Appendix. In addition, an extended version is published at arXiv:2212.09260 that describes support for the multi-objective MI ...
Hamano, Ryoki +3 more
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Enhancing Local Decisions in Agent-Based Cartesian Genetic Programming by CMA-ES
Systems, 2023 Cartesian genetic programming is a popular version of classical genetic programming, and it has now demonstrated a very good performance in solving various use cases. Originally, programs evolved by using a centralized optimization approach. Recently, an
Jörg Bremer, Sebastian Lehnhoff
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CMA-ES with Learning Rate Adaptation
ACM Transactions on Evolutionary Learning and OptimizationThe covariance matrix adaptation evolution strategy (CMA-ES) is one of the most successful methods for solving continuous black-box optimization problems. A practically useful aspect of CMA-ES is that it can be used without hyperparameter tuning.
Masahiro Nomura +2 more
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CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure [PDF]
AutoML, 2023 Many state-of-the-art automated machine learning (AutoML) systems use greedy ensemble selection (GES) by Caruana et al. (2004) to ensemble models found during model selection post hoc.
Lennart Purucker, Joeran Beel
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Analysis of modular CMA-ES on strict box-constrained problems in the SBOX-COST benchmarking suite [PDF]
GECCO Companion, 2023 Box-constraints limit the domain of decision variables and are common in real-world optimization problems, for example, due to physical, natural or spatial limitations. Consequently, solutions violating a box-constraint may not be evaluable.
D. Vermetten +4 more
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