Robustness of populations in stochastic environments
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Publication:306488
DOI10.1007/s00453-015-0072-0zbMath1360.68778OpenAlexW4383465948MaRDI QIDQ306488
Christian Gießen, Timo Kötzing
Publication date: 31 August 2016
Published in: Algorithmica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00453-015-0072-0
Analysis of algorithms and problem complexity (68Q25) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)
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Cites Work
- Robustness of populations in stochastic environments
- Simplified drift analysis for proving lower bounds in evolutionary computation
- A survey on metaheuristics for stochastic combinatorial optimization
- A study of drift analysis for estimating computation time of evolutionary algorithms
- Simulated annealing for noisy cost functions
- A simple ant colony optimizer for stochastic shortest path problems
- Multiplicative drift analysis
- The choice of the offspring population size in the \((1,\lambda)\) evolutionary algorithm
- Theoretical analysis of local search strategies to optimize network communication subject to preserving the total number of links
- Optimizing expected path lengths with ant colony optimization using fitness proportional update
- Stochastic Algorithms: Foundations and Applications
