Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm
DOI10.1007/s00453-017-0354-9zbMath1391.68100OpenAlexW2742595244MaRDI QIDQ1750362
Publication date: 18 May 2018
Published in: Algorithmica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00453-017-0354-9
genetic algorithmsparameter choiceruntime analysistheory of randomized search heuristicsparameter control
Analysis of algorithms and problem complexity (68Q25) Analysis of algorithms (68W40) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Randomized algorithms (68W20)
Related Items (23)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Crossover can provably be useful in evolutionary computation
- Optimizing linear functions with the \((1 + \lambda)\) evolutionary algorithm -- different asymptotic runtimes for different instances
- From black-box complexity to designing new genetic algorithms
- Analyzing evolutionary algorithms. The computer science perspective.
- On the analysis of the \((1+1)\) evolutionary algorithm
- Learning probability distributions in continuous evolutionary algorithms -- a comparative review
- The analysis of evolutionary algorithms -- A proof that crossover really can help
- More effective crossover operators for the all-pairs shortest path problem
- Adaptive drift analysis
- Multiplicative drift analysis
- Black-box search by unbiased variation
- Fitness levels with tail bounds for the analysis of randomized search heuristics
- Upper and lower bounds for randomized search heuristics in black-box optimization
- On the analysis of a dynamic evolutionary algorithm
- Black-box Complexity of Parallel Search with Distributed Populations
- Tight Bounds on the Optimization Time of a Randomized Search Heuristic on Linear Functions
- On the utility of the population size for inversely fitness proportional mutation rates
- Black-box search by elimination of fitness functions
- Faster black-box algorithms through higher arity operators
- Adaptive population models for offspring populations and parallel evolutionary algorithms
- Foundations of Genetic Algorithms
- Automata, Languages and Programming
- Introduction to evolutionary computing
- Drift analysis and average time complexity of evolutionary algorithms
This page was built for publication: Optimal static and self-adjusting parameter choices for the \((1+(\lambda ,\lambda ))\) genetic algorithm