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A genetic algorithms tutorial tool for numerical function optimisation

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E. K. Burke,

D. B. VarleyAuthors Info & Claims

ACM SIGCSE Bulletin, Volume 29, Issue 3

Pages 27 - 30

https://doi.org/10.1145/268809.268830

Published: 04 June 1997 Publication History

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Abstract

The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation problems over a period of generations, based upon the principles of natural selection and survival of the fittest. This paper describes an innovative tool to introduce the basics of the subject of Genetic Algorithms called GAVIn (Genetic Algorithms Visual Interface). It focuses on the domain of numerical function optimisation problems as these form a good basis for learning and operator comparison. The other problem domains are too varied and problem dependent to form a general, robust learning tool.

References

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Cited By

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Wang XMajid MJamali M(2011)Multi-core implementation of F-16 flight surface control system using Genetic Algorithm based adaptive control algorithmProceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)10.1109/NAECON.2011.6183100(191-194)Online publication date: Jul-2011
https://doi.org/10.1109/NAECON.2011.6183100
Jin HChen RZheng HChen JCheng YYu YChen TLiu X(2023)Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable NeuronsInformation Sciences10.1016/j.ins.2023.118936(118936)Online publication date: Apr-2023
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Association for Computing Machinery

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Publication History

Published: 04 June 1997

Published in SIGCSE Volume 29, Issue 3

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Wang XMajid MJamali M(2011)Multi-core implementation of F-16 flight surface control system using Genetic Algorithm based adaptive control algorithmProceedings of the 2011 IEEE National Aerospace and Electronics Conference (NAECON)10.1109/NAECON.2011.6183100(191-194)Online publication date: Jul-2011
https://doi.org/10.1109/NAECON.2011.6183100
Jin HChen RZheng HChen JCheng YYu YChen TLiu X(2023)Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable NeuronsInformation Sciences10.1016/j.ins.2023.118936(118936)Online publication date: Apr-2023
https://doi.org/10.1016/j.ins.2023.118936
Montenegro de Barros GPereira VRoboredo M(2021)ELECTRE tree: a machine learning approach to infer ELECTRE Tri-B parametersData Technologies and Applications10.1108/DTA-10-2020-025655:4(586-608)Online publication date: 30-Mar-2021
https://doi.org/10.1108/DTA-10-2020-0256
Xu KZhang DAn JLiu LLiu LWang D(2021)GenExp: Multi-objective pruning for deep neural network based on genetic algorithmNeurocomputing10.1016/j.neucom.2021.04.022451(81-94)Online publication date: Sep-2021
https://doi.org/10.1016/j.neucom.2021.04.022
Wang YYuan CLiu W(2012)Cellular Differentiation Algorithm for High Dimensional Numerical Function OptimizationProceedings of the 2012 11th International Conference on Machine Learning and Applications - Volume 0110.1109/ICMLA.2012.54(276-280)Online publication date: 12-Dec-2012
https://dl.acm.org/doi/10.1109/ICMLA.2012.54
Gardner BSimon D(2009)Evolutionary algorithm sandboxProceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics10.5555/1732323.1732421(577-582)Online publication date: 11-Oct-2009
https://dl.acm.org/doi/10.5555/1732323.1732421
Gardner BSimon D(2009)Evolutionary algorithm sandbox: A web-based graphical user interface for evolutionary algorithms2009 IEEE International Conference on Systems, Man and Cybernetics10.1109/ICSMC.2009.5346657(577-582)Online publication date: Oct-2009
https://doi.org/10.1109/ICSMC.2009.5346657
Nguyen TGordon-Brown LWheeler PPeterson J(2009)GA-SVM Based Framework for Time Series ForecastingProceedings of the 2009 Fifth International Conference on Natural Computation - Volume 0110.1109/ICNC.2009.292(493-498)Online publication date: 14-Aug-2009
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Nguyen TGordon-Brown LPeterson J(2008)The Optimal Rule Structure for Fuzzy Systems in Function Approximation by Hybrid Approach in Learning ProcessProceedings of the 2008 International Conference on Computational Intelligence for Modelling Control & Automation10.1109/CIMCA.2008.40(1211-1216)Online publication date: 10-Dec-2008
https://dl.acm.org/doi/10.1109/CIMCA.2008.40

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