First Steps Towards a Runtime Analysis of Neuroevolution
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Publication:6120967
DOI10.1145/3594805.3607125arXiv2307.00799MaRDI QIDQ6120967
Paul Fischer, Carsten Witt, Unnamed Author
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/2307.00799
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
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- Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity
- Analyzing evolutionary algorithms. The computer science perspective.
- Precision, local search and unimodal functions
- On the analysis of the \((1+1)\) evolutionary algorithm
- Static and self-adjusting mutation strengths for multi-valued decision variables
- Multiplicative drift analysis
- Self-adjusting mutation rates with provably optimal success rules
- First-hitting times under drift
- Algorithmic analysis of a basic evolutionary algorithm for continuous optimization
- (1+1) EA on Generalized Dynamic OneMax
- Training a Single Sigmoidal Neuron Is Hard
- Theory of Evolutionary Computation
- Automata, Languages and Programming
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