Article

Uniform Crossover in Genetic Algorithms

Author:

Gilbert SyswerdaAuthors Info & Claims

Proceedings of the 3rd International Conference on Genetic Algorithms

Pages 2 - 9

Published: 01 June 1989 Publication History

Abstract

No abstract available.

Cited By

View all

Wang YPei YMa ZLi JLi XHandl J(2024)A User-Guided Generation Framework for Personalized Music Synthesis Using Interactive Evolutionary ComputationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664109(1762-1769)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664109
Zheng RWang ZWilliams BChen YNeville J(2023)A generalized scalarization method for evolutionary multi-objective optimizationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i10.26474(12518-12525)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i10.26474
Nouri HGhandri ADriss OGhedira K(2023)Bi-EvoGANApplied Soft Computing10.1016/j.asoc.2023.110738147:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110738
Show More Cited By

Uniform Crossover in Genetic Algorithms
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies

Recommendations

Neural network crossover in genetic algorithms using genetic programming
Abstract
The use of genetic algorithms (GAs) to evolve neural network (NN) weights has risen in popularity in recent years, particularly when used together with gradient descent as a mutation operator. However, crossover operators are often omitted from ...
Comparison of a crossover operator in binary-coded genetic algorithms

Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to ...
A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems

A new crossover mechanism containing two operators is proposed.The new mechanism produces more cross point pairs than same point crossover.Our novel genetic algorithm has a desirable performance. Genetic Algorithm (GA) has found wide application in path ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Proceedings of the 3rd International Conference on Genetic Algorithms

June 1989

438 pages

ISBN:1558600663

Editor:
J. David Schaffer

Publisher

Morgan Kaufmann Publishers Inc.

San Francisco, CA, United States

Publication History

Published: 01 June 1989

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

173
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 06 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Wang YPei YMa ZLi JLi XHandl J(2024)A User-Guided Generation Framework for Personalized Music Synthesis Using Interactive Evolutionary ComputationProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664109(1762-1769)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3664109
Zheng RWang ZWilliams BChen YNeville J(2023)A generalized scalarization method for evolutionary multi-objective optimizationProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i10.26474(12518-12525)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i10.26474
Nouri HGhandri ADriss OGhedira K(2023)Bi-EvoGANApplied Soft Computing10.1016/j.asoc.2023.110738147:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110738
Guo YLiu FXiao NLi ZCai ZTang GLiu N(2022)PARAInternational Journal of Intelligent Systems10.1002/int.2295437:11(8523-8547)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1002/int.22954
Corus DLissovoi AOliveto PWitt C(2021)On Steady-State Evolutionary Algorithms and Selective Pressure: Why Inverse Rank-Based Allocation of Reproductive Trials Is BestACM Transactions on Evolutionary Learning and Optimization10.1145/34274741:1(1-38)Online publication date: 26-Apr-2021
https://dl.acm.org/doi/10.1145/3427474
Xie YNeumann ANeumann FCoello Coello C(2020)Specific single- and multi-objective evolutionary algorithms for the chance-constrained knapsack problemProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390162(271-279)Online publication date: 25-Jun-2020
https://dl.acm.org/doi/10.1145/3377930.3390162
Terra-Neves MLynce IManquinho V(2019)Integrating Pseudo-Boolean constraint reasoning in multi-objective evolutionary algorithmsProceedings of the 28th International Joint Conference on Artificial Intelligence10.5555/3367032.3367200(1184-1190)Online publication date: 10-Aug-2019
https://dl.acm.org/doi/10.5555/3367032.3367200
Lian YHuang ZZhou YChen Z(2019)Improve Theoretical Upper Bound of Jumpk Function by Evolutionary MultitaskingProceedings of the 2019 3rd High Performance Computing and Cluster Technologies Conference10.1145/3341069.3342982(44-50)Online publication date: 22-Jun-2019
https://dl.acm.org/doi/10.1145/3341069.3342982
Masegosa AOsaba EAngarita-Zapata JLaña ISer JAuger AStützle T(2019)Nature-inspired metaheuristics for optimizing information dissemination in vehicular networksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3326847(1312-1320)Online publication date: 13-Jul-2019
https://dl.acm.org/doi/10.1145/3319619.3326847
De Maio VBrandic IApte VDi Marco ALitoiu MMerseguer J(2019)Multi-Objective Mobile Edge Provisioning in Small Cell CloudsProceedings of the 2019 ACM/SPEC International Conference on Performance Engineering10.1145/3297663.3310301(127-138)Online publication date: 4-Apr-2019
https://dl.acm.org/doi/10.1145/3297663.3310301
Show More Cited By

Abstract

Cited By

Recommendations

Neural network crossover in genetic algorithms using genetic programming

Comparison of a crossover operator in binary-coded genetic algorithms

A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations