skip to main content
10.1145/1276958.1277186acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

The defined cliffs variant in dynamic environments: a case study using the shaky ladder hyperplane-defined functions

Published: 07 July 2007 Publication History

Abstract

The shaky ladder hyperplane-defined functions (sl-hdfs) are a test suite utilized for exploring the behavior of the genetic algorithm (GA) in dynamic environments. This test suite can generate arbitrary problems with similar levels of difficulty and it provides a platform for systematic controlled observations of the GA in dynamic environments. Previous work has found two factors that contribute to the GA's success on sl-hdfs: (1) short initial building blocks and (2) significantly changing the reward structure during fitness landscape changes. Therefore a test function that combines these two features should facilitate even better GA performance. This has led to the construction of a new sl-hdf variant, "Defined Cliffs," in which we combine short elementary building blocks with sharp transitions in the environment. We examine this variant with two different levels of dynamics, static and regularly changing, using four different metrics. The results show superior GA performance on the Defined Cliffs over all previous variants (Cliffs, Weight, and Smooth). Our observations and conclusions in this variant further the understanding of the GA in dynamic environments.

References

[1]
Branke, J.: Evolutionary Optimization in Dynamic Environments. Kluwer Academic Publishers (2001).
[2]
Holland, J.H.: Building blocks, cohort genetic algorithms, and Hyperplane-defined functions. Evolutionary Computation 8 (2000) 373--391.
[3]
Rand, W., Riolo, R.: Shaky ladders, Hyperplane-defined functions and genetic algorithms: Systematic controlled observation in dynamic environments. In Rothlauf,allF.et al., ed.: Applications of Evolutionary Computing, Evoworkshops: EvoBIO, EvoCOMNET, EvoHot, EvoIASP, EvoMUSART, and EvoSTOC. Volume 3449 ofallLecture Notes In Computer Science., Springer (2005).
[4]
Rand, W., Riolo, R.: The problem with a self-adaptive mutation rate in some environments: A case study using the shaky ladder Hyperplane-defined functions. In Beyer, H.G.et al., ed.: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005, New York, ACM Press (2005).
[5]
Rand, W., Riolo, R.: Measurements for understanding the behavior of the genetic algorithm in dynamic environments: A case study using the shaky ladder Hyperplane-defined functions. In Beyer, H.G.et al., ed.: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2005, New York, ACM Press (2005).
[6]
Rand, W.: Controlled Observations of the Genetic Algorithm in a Changing Environment: Case Studies Using the Shaky Ladder Hyperplane-Defined Functions. PhD thesis, University of Michigan (2005).
[7]
Rand, W., Riolo, R.L.: The Effect of Building Block Construction on the Behavior of the GA Shaky Ladder Hyperplane Defined Functions. In: EvoWorkshops. (2006) 776--787.
[8]
Stanhope, S.A., Daida, J.M.: Optimal mutation and crossover rates for a genetic algorithm operating in a dynamic environment. In: Evolutionary Programming VII. Number 1447 in LNCS, Springer (1998) 693--702.
[9]
Whitley, D., Rana, S.B., Dzubera, J., Mathias, K.E.: Evaluating evolutionary algorithms. Artificial Intelligence 85 (1996) 245--276.

Index Terms

  1. The defined cliffs variant in dynamic environments: a case study using the shaky ladder hyperplane-defined functions

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 07 July 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. building blocks
    2. dynamic environments
    3. genetic algorithms
    4. shaky ladder hyperplane-defined functions

    Qualifiers

    • Article

    Conference

    GECCO07
    Sponsor:

    Acceptance Rates

    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 102
      Total Downloads
    • Downloads (Last 12 months)1
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 16 Feb 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media