[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Compressed-Objective Genetic Algorithm

  • Conference paper
Parallel Problem Solving from Nature - PPSN IX (PPSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4193))

Included in the following conference series:

Abstract

A strategy for solving an optimisation problem with a large number of objectives by transforming the original objective vector into a two-objective vector during survival selection is presented. The transformed objectives, referred to as preference objectives, consist of a winning score and a vicinity index. The winning score, a maximisation criterion, describes the difference of the number of superior and inferior objectives between two solutions. The minimisation vicinity index describes the level of solution clustering around a search location, particularly the best value of each individual objective, is used to encourage the results to spread throughout the Pareto front. With this strategy, a new multi-objective algorithm, the compressed-objective genetic algorithm (COGA), is introduced. COGA is subsequently benchmarked against a non-dominated sorting genetic algorithm II (NSGA-II) and an improved strength Pareto genetic algorithm (SPEA-II) in six scalable DTLZ benchmark problems with three to six objectives. The results reveal that the proposed strategy plays a crucial role in the generation of a superior solution set compared to the other two techniques in terms of the solution set coverage and the closeness to the true Pareto front. Furthermore, the spacing of COGA solutions is very similar to that of SPEA-II solutions. Overall, the functionality of the multi-objective evolutionary algorithm (MOEA) with preference objectives is effectively demonstrated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 71.50
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 89.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Horn, J., Nafpliotis, N.: Multiobjective optimization using the niched Pareto genetic algorithm. IlliGAL Report No. 93005, Illinois Genetic Algorithms Laboratory, University of Illinois at Urbana-Champaign, IL (1993)

    Google Scholar 

  2. Srinivas, N., Deb, K.: Multi-objective function optimization using non-dominated sorting genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)

    Article  Google Scholar 

  3. Fonseca, C.M., Fleming, P.J.: Multiobjective optimization and multiple constraint handling with evolutionary algorithms–Part 1: A unified formulation. IEEE Trans. Syst. Man Cybern. A Syst. Hum. 28(1), 26–37 (1998)

    Article  Google Scholar 

  4. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)

    Article  Google Scholar 

  5. Deb, K., Goel, T.: Controlled Elitist Non-dominated Sorting Genetic Algorithms for Better Convergence. In: Zitzler, E., Deb, K., Thiele, L., Coello Coello, C.A., Corne, D.W. (eds.) EMO 2001. LNCS, vol. 1993, pp. 67–81. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  6. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)

    Article  Google Scholar 

  7. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Giannakoglou, K., Tsahalis, D., Periaux, J., Papailiou, K., Fogarty, T. (eds.) Evolutionary Methods for Design, Optimisation and Control, CIMNE, Barcelona, Spain, pp. 95–100 (2002)

    Google Scholar 

  8. Chaiyaratana, N., Piroonratana, T., Sangkawelert, N.: Effects of diversity control in single-objective and multi-objective genetic algorithms. J. Heuristics (in press, 2007)

    Google Scholar 

  9. Deb, K., Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Abraham, A., Jain, L.C., Goldberg, R. (eds.) Evolutionary Multiobjective Optimization: Theoretical Advances and Applications, pp. 105–145. Springer, London (2005)

    Chapter  Google Scholar 

  10. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: Empirical results. Evol. Comput. 8(2), 173–195 (2000)

    Article  Google Scholar 

  11. Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)

    MATH  Google Scholar 

  12. Purshouse, R.C., Fleming, P.J.: Conflict, harmony, and independence: Relationships in evolutionary multi-criterion optimisation. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Deb, K., Thiele, L. (eds.) EMO 2003. LNCS, vol. 2632, pp. 16–30. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Maneeratana, K., Boonlong, K., Chaiyaratana, N. (2006). Compressed-Objective Genetic Algorithm. In: Runarsson, T.P., Beyer, HG., Burke, E., Merelo-Guervós, J.J., Whitley, L.D., Yao, X. (eds) Parallel Problem Solving from Nature - PPSN IX. PPSN 2006. Lecture Notes in Computer Science, vol 4193. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11844297_48

Download citation

  • DOI: https://doi.org/10.1007/11844297_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-38990-3

  • Online ISBN: 978-3-540-38991-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics