On the benefits of populations for the exploitation speed of standard steady-state genetic algorithms
Author:
Affiliation:
1. University of Sheffield, Sheffield
Funder
EPSRC
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3321707.3321783
Reference28 articles.
1. Level-Based Analysis of Genetic Algorithms and Other Search Processes
2. Standard Steady State Genetic Algorithms Can Hillclimb Faster Than Mutation-Only Evolutionary Algorithms
3. Escaping Local Optima Using Crossover With Emergent Diversity
4. Optimal Parameter Choices Through Self-Adjustment
5. A Tight Runtime Analysis of the (1+(λ, λ)) Genetic Algorithm on OneMax
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