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Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control.

, , , and . CoRR, (2015)

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Reinforcement Learning for N-player Games: The Importance of Final Adaptation., and . BIOMA, volume 12438 of Lecture Notes in Computer Science, page 84-96. Springer, (2020)Solving optimization problems with high conditioning by means of online whitening., , and . GECCO (Companion), page 243-244. ACM, (2019)SACOBRA with Online Whitening for Solving Optimization Problems with High Conditioning., , and . CoRR, (2019)Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control., , , and . CoRR, (2015)Temporal difference learning with eligibility traces for the game connect four., , , and . CIG, page 1-8. IEEE, (2014)Online Adaptable Learning Rates for the Game Connect-4., , , and . IEEE Trans. Comput. Intell. AI Games, 8 (1): 33-42 (2016)Surrogate-assisted optimization for augmentation of finite element techniques., , , and . J. Comput. Sci., (2021)Equality constraint handling for surrogate-assisted constrained optimization., , and . CEC, page 1924-1931. IEEE, (2016)Online selection of surrogate models for constrained black-box optimization., , and . SSCI, page 1-8. IEEE, (2016)Self-adjusting parameter control for surrogate-assisted constrained optimization under limited budgets., , , and . Appl. Soft Comput., (2017)