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Fitness inheritance in genetic algorithms

Authors:

Robert E. Smith,

B. A. Dike,

S. A. StegmannAuthors Info & Claims

SAC '95: Proceedings of the 1995 ACM symposium on Applied computing

Pages 345 - 350

https://doi.org/10.1145/315891.316014

Published: 26 February 1995 Publication History

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Cited By

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Tzruia IHalperin TSipper MElyasaf A(2024)Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary StateInformation10.3390/info1512074415:12(744)Online publication date: 21-Nov-2024
https://doi.org/10.3390/info15120744
Le Clei MBar-Sheshet SBellec PLi XHandl J(2024)Generational Information Transfer with Neuroevolution on Control TasksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654415(595-598)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654415
Yousef MAl Shehab LGhani DAlazzam HGhatasheh M(2024)Enhancing Autism Disease Classification Using a Hybrid GA-KNN Approach for Feature Selection2024 15th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS63486.2024.10638319(1-7)Online publication date: 13-Aug-2024
https://doi.org/10.1109/ICICS63486.2024.10638319
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Fitness inheritance in genetic algorithms

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Abstract
This research builds on the hypothesis that the use of different fitness measures on the different generations of genetic programming (GP) is more effective than the convention of applying the same fitness measure individually ...

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Information

Published In

SAC '95: Proceedings of the 1995 ACM symposium on Applied computing

February 1995

604 pages

ISBN:0897916581

DOI:10.1145/315891

Chairmen:
Jim Hightower
California State Univ.-Fullerton, Fullerton, CA
,
Ed Deaton
Hope College, Holland, MI
,
Editors:
K. M. George
Oklahoma State Univ., Stillwater, OK
,
Janice Carroll
Sam Houston State Univ., Huntsville, TX
,
Dave Oppenheim
ACM Local Activities Board

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 1995

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SAC95: 1995 Symposium on Applied Computing

February 26 - 28, 1995

Tennessee, Nashville, USA

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all

Tzruia IHalperin TSipper MElyasaf A(2024)Fitness Approximation Through Machine Learning with Dynamic Adaptation to the Evolutionary StateInformation10.3390/info1512074415:12(744)Online publication date: 21-Nov-2024
https://doi.org/10.3390/info15120744
Le Clei MBar-Sheshet SBellec PLi XHandl J(2024)Generational Information Transfer with Neuroevolution on Control TasksProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3654415(595-598)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638530.3654415
Yousef MAl Shehab LGhani DAlazzam HGhatasheh M(2024)Enhancing Autism Disease Classification Using a Hybrid GA-KNN Approach for Feature Selection2024 15th International Conference on Information and Communication Systems (ICICS)10.1109/ICICS63486.2024.10638319(1-7)Online publication date: 13-Aug-2024
https://doi.org/10.1109/ICICS63486.2024.10638319
Coello Coello C(2023)Recent Research Topics in Evolutionary Multiobjective Optimization: A Personal PerspectiveComputational Intelligence10.1007/978-3-031-46221-4_5(90-120)Online publication date: 3-Nov-2023
https://doi.org/10.1007/978-3-031-46221-4_5
Xue XYang CHu YZhang KCheung YSong LTan K(2022)Evolutionary Sequential Transfer Optimization for Objective-Heterogeneous ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2021.313387426:6(1424-1438)Online publication date: Dec-2022
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Paletti DPeverelli FConficconi D(2022)Online Learning RTL Synthesis for Automated Design Space Exploration2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)10.1109/IPDPSW55747.2022.00021(69-76)Online publication date: May-2022
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Gallotta RArulkumaran KSoros L(2022)Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation2022 IEEE Conference on Games (CoG)10.1109/CoG51982.2022.9893592(500-503)Online publication date: 21-Aug-2022
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Hassan APillay N(2022)Hybridizing A Genetic Algorithm With Reinforcement Learning for Automated Design of Genetic Algorithms2022 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC55065.2022.9870302(1-8)Online publication date: 18-Jul-2022
https://dl.acm.org/doi/10.1109/CEC55065.2022.9870302
Li JZhan ZZhang J(2022)Evolutionary Computation for Expensive Optimization: A SurveyMachine Intelligence Research10.1007/s11633-022-1317-419:1(3-23)Online publication date: 21-Jan-2022
https://doi.org/10.1007/s11633-022-1317-4
Virolainen S(2021) A mixture autoregressive model based on Gaussian and Student’s t -distributions Studies in Nonlinear Dynamics & Econometrics10.1515/snde-2020-006026:4(559-580)Online publication date: 15-Jul-2021
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Research and implementation on genetic algorithms for graph fitness optimization

Genetic algorithms for large join query optimization

Evolving dynamic fitness measures for genetic programming