Corpus ID: 5680795

A parameter-less genetic algorithm

@inproceedings{Harik1999APG,
  title={A parameter-less genetic algorithm},
  author={G. Harik and F. Lobo},
  booktitle={GECCO},
  year={1999}
}
From the user's point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes, crossover probabilities, selection rates, and other GA technicalities. He is just interested in solving a problem, and what he would really like to do, is to hand-in the problem to a blackbox algorithm, and simply press a start button. This paper explores the development of a GA that fulfills this requirement. It has no… Expand
268 Citations
A Genetic Algorithm With Self-Generated Random Parameters
  • 6
  • PDF
A Compass to Guide Genetic Algorithms
  • 70
  • PDF
New Ways to Calibrate Evolutionary Algorithms
  • Gusz Eiben, M. Schut
  • Biology, Computer Science
  • Advances in Metaheuristics for Hard Optimization
  • 2008
  • 51
  • PDF
Quality-Time Tradeoff in a Distributed Parameter-less Genetic Algorithm
  • G. Mitchell
  • Computer Science
  • Artificial Intelligence and Applications
  • 2005
  • 3
  • PDF
Initial Population for Genetic Algorithms: A Metric Approach
  • 114
  • PDF
The parameter-less genetic algorithm in practice
  • 91
  • PDF
A parameter-less genetic algorithm with customized crossover and mutation operators
  • 16
  • PDF
Parameter-less algorithm for evolutionary-based optimization
  • G. Papa
  • Mathematics, Computer Science
  • Comput. Optim. Appl.
  • 2013
  • 16
A Study of Adaptation and Random Search in Genetic Algorithms
  • 8
  • Highly Influenced
Parameter-less late acceptance hill-climbing
  • 4
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 34 REFERENCES
Adaptively Resizing Populations: Algorithm, Analysis, and First Results
  • 69
  • PDF
The gambler's ruin problem, genetic algorithms, and the sizing of populations
  • 249
Genetic Algorithms, Noise, and the Sizing of Populations
  • 747
  • PDF
Noise, sampling, and efficient genetic algorthms
  • 112
Adapting Operator Probabilities in Genetic Algorithms
  • 684
How Genetic Algorithms Really Work I.mutation and Hillclimbing
  • 307
  • PDF
Optimization of Control Parameters for Genetic Algorithms
  • J. Grefenstette
  • Computer Science
  • IEEE Transactions on Systems, Man, and Cybernetics
  • 1986
  • 2,897
Optimal Mutation Rates in Genetic Search
  • 350
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
  • 2,419
  • PDF
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
  • 223
...
1
2
3
4
...