skip to main content
10.1145/3067695.3076045acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
poster

Interpolated continuous optimisation problems with tunable landscape features

Published: 15 July 2017 Publication History

Abstract

In this paper, we introduce a new class of optimisation problems with tunable landscape features called Interpolated Continuous Optimisation Problems (ICOPs). ICOPs are defined by a search space, a set of solutions called seeds at selected positions, and their fitnesses. The rest of the fitness landscape is interpolated from the seeds using the inverse distance weighting interpolation function. We show that by evolving the position and the fitness of the seeds, we can generate extreme problems with respect to different fitness landscape measures.

References

[1]
Yossi Borenstein and Riccardo Poli. 2005. Information landscapes. In Proceedings of the 7th annual conference on genetic and evolutionary computation. ACM, 1515--1522.
[2]
Robert W Garden and Andries P Engelbrecht. 2014. Analysis and classification of optimisation benchmark functions and benchmark suites. In 2014 IEEE Congress on Evolutionary computation. IEEE, 1641--1649.
[3]
Nikolaus Hansen, Anne Auger, Olaf Mersmann, Tea Tusar, and Dimo Brockhoff. 2016. COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting. ArXiv e-prints (March 2016). arXiv:cs.AI/1603.08785
[4]
Terry Jones and Stephanie Forrest. 1995. Fitness distance correlation as a measure of problem difficulty for genetic algorithms. In Proceedings of the 6th international conference on genetic algorithms. ACM, 184fi?!--192.
[5]
William B. Langdon and Riccardo Poli. 2007. Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms. IEEE Transactions on Evolutionary Computation 11, 5 (Oct 2007), 561--578.
[6]
Monte Lunacek and Darrell Whitley. 2006. The dispersion metric and the CMA evolution strategy. In Proceedings of the 8th annual conference on genetic and evolutionary computation. ACM, 477--484.
[7]
John A. W. McCall, Lee A. Christie, and Alexander E. I. Brownlee. 2015. Generating easy and hard problems using the proximate optimality principle. In Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation. ACM, 767--768.
[8]
Donald Shepard. 1968. A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 23rd ACM national conference. ACM, 517--524.
[9]
Rainer Storn and Kenneth Price. 1997. Differential Evolution - A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization 11, 4 (1997), 341--359.
[10]
Ponnuthurai N. Suganthan, Nikolaus Hansen, J J Liang, Kalyanmoy Deb, Ying-Ping Chen, Anne Auger, and Santosh Tiwari. 2005. Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization. Technical Report.
[11]
Simon Wessing, Mike Preuss, and Gunter Rudolph. 2013. Niching by multiobjectivization with neighbor information: Trade-offs and benefits. In 2013 IEEE Congress on Evolutionary Computation. 103--110.

Cited By

View all
  • (2022)Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm SelectionMathematics10.3390/math1003043210:3(432)Online publication date: 29-Jan-2022
  • (2020)Upper confidence tree for planning restart strategies in multi-modal optimizationSoft Computing10.1007/s00500-020-05196-wOnline publication date: 23-Jul-2020
  • (2020)Comparative Run-Time Performance of Evolutionary Algorithms on Multi-objective Interpolated Continuous Optimisation ProblemsParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58112-1_20(287-300)Online publication date: 31-Aug-2020
  • Show More Cited By

Index Terms

  1. Interpolated continuous optimisation problems with tunable landscape features

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion
      July 2017
      1934 pages
      ISBN:9781450349390
      DOI:10.1145/3067695
      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 15 July 2017

      Check for updates

      Author Tags

      1. benchmark functions
      2. continuous optimisation
      3. differential evolution
      4. dispersion metric
      5. fitness distance correlation
      6. information landscape
      7. metrics
      8. numerical optimisation

      Qualifiers

      • Poster

      Conference

      GECCO '17
      Sponsor:

      Acceptance Rates

      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)8
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 16 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2022)Transfer Learning Analysis of Multi-Class Classification for Landscape-Aware Algorithm SelectionMathematics10.3390/math1003043210:3(432)Online publication date: 29-Jan-2022
      • (2020)Upper confidence tree for planning restart strategies in multi-modal optimizationSoft Computing10.1007/s00500-020-05196-wOnline publication date: 23-Jul-2020
      • (2020)Comparative Run-Time Performance of Evolutionary Algorithms on Multi-objective Interpolated Continuous Optimisation ProblemsParallel Problem Solving from Nature – PPSN XVI10.1007/978-3-030-58112-1_20(287-300)Online publication date: 31-Aug-2020
      • (2019)Limitations of benchmark sets and landscape features for algorithm selection and performance predictionProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3319619.3322051(261-262)Online publication date: 13-Jul-2019
      • (2018)Investigating benchmark correlations when comparing algorithms with parameter tuningProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3205651.3205747(209-210)Online publication date: 6-Jul-2018

      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