Using Automated Algorithm Configuration for Parameter Control
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Publication:6202151
DOI10.1145/3594805.3607127arXiv2302.12334MaRDI QIDQ6202151
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Publication date: 23 February 2024
Published in: Proceedings of the 17th ACM/SIGEVO Conference on Foundations of Genetic Algorithms (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2302.12334
Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
Cites Work
- From black-box complexity to designing new genetic algorithms
- Analyzing bandit-based adaptive operator selection mechanisms
- Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm
- Learning probability distributions in continuous evolutionary algorithms -- a comparative review
- Black-box search by unbiased variation
- A deep reinforcement learning based hyper-heuristic for combinatorial optimisation with uncertainties
- A rigorous runtime analysis of the \((1 + (\lambda, \lambda))\) GA on jump functions
- Fast mutation in crossover-based algorithms
- Playing mastermind with constant-size memory
- Analyzing randomized search heuristics via stochastic domination
- Capping methods for the automatic configuration of optimization algorithms
- An Experimental Study of Operator Choices in the $$(1+(\lambda ,\lambda ))$$ Genetic Algorithm
- A tight runtime analysis for the (1 + (λ, λ)) GA on leadingones
- Automated Dynamic Algorithm Configuration
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