skip to main content
10.1145/1276958.1277111acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

COSMO: a correlation sensitive mutation operator for multi-objective optimization

Published: 07 July 2007 Publication History

Abstract

This contribution is the first to discover exploitable structural features within circuit optimization problems (COP) and discuss how it is indicative of a general structure and possibly a 'measure of hardness' in real-world multi-objective optimization problems. We then present a methodology to exploit this structure in a multi-objective evolutionary algorithm by designing a novel Correlation Sensitive Mutation Operator, COSMO. COSMO is, at the least, universally applicable in the domain of circuits and we discuss how it can be easily extended to other domains. We discuss the rationale behind COSMO and interpret it in context of dimensional locality. We compare COSMO's performance with the traditional operators used for multi-objective optimization. For two instances of circuits, we show that COSMO gives significantly faster and better optimization than conventional operators. The paper also takes the first steps in thinking and interpreting how operators for MO-EAs should be designed.

References

[1]
V. Aggarwal and U.-M. O'Reilly. Monotonicity information in Simulation-based approaches for efficient circuit sizing and hierarchical synthesis. In In submission to ICCAD, 2007.
[2]
T. Baeck and H.-P. Schwefel. An overview of evolutionary algorithms for parameter optimization. Evol. Comput., 1(1):1--23, 1993.
[3]
P. Bosman and D. Thierens. Numerical optimization with real-valued estimation-of-distribution algorithms. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications, pages 91--120, 2006.
[4]
S. Boyd, S.-J. Kim, L. Vaudenberghe, and A. Hassibi. A tutorial on geometric programming. In Technical report, EE Department, Stanford University, 2004.
[5]
M. Chu, D. J. Allstot, J. M. Huard, and K. Y. Wong. NSGA-based parasitic-aware optimization of a 5GHz low-noise VCO. ASP-DAC, 00:169--173, 2004.
[6]
K. Deb. Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, Inc., New York, NY, USA, 2001.
[7]
K. Deb and R. B. Agrawal. Simulated binary crossover for continuous search space. Complex Systems, 9:115--148, 1995.
[8]
K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation:NSGA-II. In PPSN, pages 849--858, 2000.
[9]
K. Deb and H. Beyer. Self-adaptive genetic algorithms with simulated binary crossover. Technical Report No. CI-61/99, University of Dortmund, Department of Computer Science/LS11, 1999.
[10]
T. Eeckelaert, T. McConaghy, and G. Gielen. Efficient multiobjective synthesis of analog circuits using hierarchical pareto-optimal performance hypersurfaces. In DATE'05, pages 1070--1075, 2005.
[11]
D. E. Goldberg. The Design of Innovation: Lessons from and for Competent Genetic Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 2002.
[12]
N. Hansen, S. Muller, and P. Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation. Evolutionary Computation 11 (2003)1--18, 2003.
[13]
J. R. Hellums, L. R. Carley, M. J. Krasnicki, R. A. Rutenbar, and R. Phelps. A case study of synthesis for industrial-scale analog IP:Redesign of the equalizer/filter frontend for an ADSL CODEC. DAC, 00:1--6, 2000.
[14]
C. Igel, N. Hansen, and S. Roth. Covariance matrix adaptation for multi-objective optimization. to appear in Evolutionary Computation, 2007.
[15]
Y. Jin and B. Sendho. Connectedness, regularity and the success of local search in evolutionary multi-objective optimization. In Proceedings of IEEE Congress on Evol. Comput., pages 1910--1917, 2003.
[16]
J. Kennedy and R. Eberhart. Particle swarm optimization. In Proceedings of the Fourth IEEE International Conference on Neural Networks.
[17]
J. D. Knowles and D. W. Corne. Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput., 8(2):149--172, 2000.
[18]
F. Leyn, G. G. E. Gielen, andW. M. C. Sansen. An efficient DC root solving algorithm with guaranteed convergence for analog integrated CMOS circuits. In ICCAD, pages 304--307, 1998.
[19]
M. Pelikan, K. Sastry, and E. C. Paz. Scalable Optimization via Probabilistic Modeling: From Algorithms to Applications. Springer-Verlag, Berlin Heidelberg, 2006.
[20]
S. K. Tiwary, P. K. Tiwary, and R. A. Rutenbar. Generation of yield-aware pareto surfaces for hierarchical circuit design space exploration. In DAC, pages 31--36, 2006.
[21]
E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. In EUROGEN 2001, Evolutionary Methods for Design, Optimization and Control with Application to Industrial Problems, pages 95--100, Greece, 2002.

Cited By

View all
  • (2010)Optimising multi-modal polynomial mutation operators for multi-objective problem classesIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586076(1-8)Online publication date: Jul-2010
  • (2008)A Pareto following variation operator for evolutionary dynamic multi-objective optimization2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)10.1109/CEC.2008.4631100(2270-2277)Online publication date: Jun-2008

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. circuits
  2. evolutionary
  3. fitness landscape
  4. monotonic functions
  5. multi-objective optimization
  6. operators

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

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2010)Optimising multi-modal polynomial mutation operators for multi-objective problem classesIEEE Congress on Evolutionary Computation10.1109/CEC.2010.5586076(1-8)Online publication date: Jul-2010
  • (2008)A Pareto following variation operator for evolutionary dynamic multi-objective optimization2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)10.1109/CEC.2008.4631100(2270-2277)Online publication date: Jun-2008

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