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Combining Local and Global Search: A Multi-objective Evolutionary Algorithm for Cartesian Genetic Programming

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Inspired by Nature

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 28))

Abstract

This work investigates the effects of the periodization of local and global multi-objective search algorithms. We rely on a model for periodization and define a multi-objective evolutionary algorithm adopting concepts from Evolutionary Strategies and NSGAII. We show that our method excels for the evolution of digital circuits on the Cartesian Genetic Programming model as well as on some standard benchmarks such as the ZDT6, especially when periodized with standard multi-objective genetic algorithms.

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References

  1. Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E.: PISA—a platform and programming language independent interface for search algorithms. In: Intlernational Conference on Evolutionary Multi-Criterion Optimization (EMO) LNCS, pp. 494–508. Springer (2003)

    Google Scholar 

  2. Conover, W.J., Practical Nonparametric Statistics (3rd edn.). Wiley (1999)

    Google Scholar 

  3. Deb, K., Agrawal, R.B.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995)

    MATH  MathSciNet  Google Scholar 

  4. Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. In: Parallel Problem Solving from Nature (PPSN’00), pp. 849–858. Springer (2000)

    Google Scholar 

  5. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms. Wiley, Inc (2001)

    Google Scholar 

  6. Deb, K.,Thiele, L., Laumanns, M., Zitzler, E.: Scalable test problems for evolutionary multi-objective optimization. In: Evolutionary Multiobjective Optimization: theoretical Advances and Applications, chap. 6, pp. 105–145. Springer (2005)

    Google Scholar 

  7. García, A., Díaz, H., Luis, V., Quintero, S., Carlos, A., Coello, C., Caballero, R., Luque, J.M.: A new proposal for multi-objective optimization using differential evolution and rough sets theory. In: Genetic and Evolutionary Computation (GECCO), pp. 675–682. ACM (2006)

    Google Scholar 

  8. Harada, K., Ikeda, K., Kobayashi, S.: Hybridization of genetic algorithm and llocal search in multiobjective function optimization: recommendation of GA then LS. In: Genetic and Evolutionary Computation (GECCO), pp. 667–674. ACM (2006)

    Google Scholar 

  9. Ishibuchi, H., Narukawa, K.: Some issues on the implementation of local search in evolutionary multiobjective optimization. In: Genetic and Evolutionary Computation (GECCO), LNCS, pp. 1246–1258. Springer (2004)

    Google Scholar 

  10. Ishibuchi, H., Yoshida, T., Murata, T.: Balance between genetic search and local search in hybrid evolutionary multi-criterion optimization Algorithms. In: Genetic and Evolutionary Computation (GECCO), pp. 1301–1308. Morgan Kaufmann Publishers (2002)

    Google Scholar 

  11. Paul, K.: Adapting Hardware Systems by Means of Multi-Objective Evolution. Logos Verlag, Berlin (2013)

    Google Scholar 

  12. Knieper, T., Defo, B., Kaufmann, P., Platzner, M.: On robust evolution of digital hardware. In: Biologically Inspired Collaborative Computing (BICC), vol. 268 of IFIP International Federation for Information Processing, pp. 2313–222. Springer (2008)

    Google Scholar 

  13. Kaufmann, P., Knieper, T., Platzner, M.: A novel hybrid evolutionary strategy and its periodization with multi-objective genetic optimizers. In: IEEE World Congress on Computational Intelligence (WCCI), Congress on Evolutionary Computation (CEC), pp. 541–548. IEEE (2010)

    Google Scholar 

  14. Kaufmann, P., Platzner, M.: Multi-objective Intrinsic Hardware Evolution. In: International Conference Military Applications of Programmable Logic Devices (MAPLD) (2006)

    Google Scholar 

  15. Kaufmann, P., Platzner, M.: MOVES: a modular framework for hardware evolution. In: IEEE Adaptive Hardware and Systems (AHS), pp. 447–454. IEEE (2007)

    Google Scholar 

  16. Kaufmann, P., Platzner, M.: Toward self-adaptive embedded systems: multi-objective hardware evolution. In: Architecture of Computing Systems (ARCS), vol. 4415 of LNCS, pp. 199–208. Springer (2007)

    Google Scholar 

  17. Kaufmann, P., Plessl, C., Platzner, M.: EvoCaches: application-specific adaptation of cache mappings. In: IEEE Adaptive Hardware and Systems (AHS), pp. 11–18. IEEE, CS (2009)

    Google Scholar 

  18. Knowles, J., Thiele, L., Zitzler, E.: A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers. Technical report, Computer Engineering and Networks Laboratory (TIK), ETH Zurich, Switzerland (2006)

    Google Scholar 

  19. Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. Wiley, Inc (1990)

    Google Scholar 

  20. Miller, J., Thomson, P.: Cartesian genetic programming. In: European Conference on Genetic Programming (EuroGP), pp. 121–132. Springer (2000)

    Google Scholar 

  21. Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7(4), 308–313 (1965)

    Article  MATH  MathSciNet  Google Scholar 

  22. Scott, J.R.: Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization. Master’s thesis, Department of Aeronautics and Astronautics. Massachusetts Institute of Technology (1995)

    Google Scholar 

  23. Shaw, K.J., Nortcliffe, A.L., Thompson, M., Love, J., Fonseca, C.M.,. Fleming, P.J.: Assessing the performance of multiobjective genetic algorithms for optimization of a batch process scheduling problem. In: Evolutionary Computation, pp. 37–45. IEEE (1999)

    Google Scholar 

  24. Lukas, K., Walker, J.A., Kaufmann, P., Plessl, C., Platzner, M.: Evolution of Electronic Circuits. Cartesian Genetic Programming. Natural Computing Series, pp. 125–179. Springer, Berlin (2011)

    Google Scholar 

  25. Talbi, El-G., Rahoual, M., Mabed, M.H., Dhaenens, M.C: A hybrid evolutionary approach for multicriteria optimization problems: application to the flow shop. In: International Conference on Evolutionary Multi-Criterion Optimization (EMO), pp. 416–428. Springer (2001)

    Google Scholar 

  26. David, A, Veldhuizen, V.: Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. PhD thesis, Department of Electrical and Computer Engineering. Airforce Institute of Technology (1999)

    Google Scholar 

  27. Walker, J.A., Hilder, J.A., Tyrrell, A.M: Towards evolving industry-feasible intrinsic variability tolerant cmos designs. In: 2009 IEEE Congress on Evolutionary Computation, pp. 1591–1598 (May 2009)

    Google Scholar 

  28. Zitzler, E., Deb, K., Thiele, L.: Comparison of multiobjective evolutionary algorithms: empirical results. In: Evolutionary Computation, vol. 8(2), pp. 173–195. MIT Press (2000)

    Google Scholar 

  29. Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. Tech. Rep. 103, ETH Zurich (2001)

    Google Scholar 

  30. Martínez, S.Z., Carlos, A., Coello, C.: A proposal to hybridize multi-objective evolutionary algorithms with non-gradient mathematical programming techniques. In: Parallel Problem Solving from Nature (PPSN’08), pp 837–846. Springer (2008)

    Google Scholar 

  31. Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. In: IEEE Transcations on Evolutionary Computation, vol. 3(4), pp. 257–271. IEEE 1999

    Google Scholar 

  32. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany

    Paul Kaufmann & Marco Platzner

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  1. Paul Kaufmann
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  2. Marco Platzner
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Correspondence to Paul Kaufmann .

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Editors and Affiliations

  1. Department of Computer Science, University of York, York, United Kingdom

    Susan Stepney

  2. Unconventional Computing Centre, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

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Kaufmann, P., Platzner, M. (2018). Combining Local and Global Search: A Multi-objective Evolutionary Algorithm for Cartesian Genetic Programming. In: Stepney, S., Adamatzky, A. (eds) Inspired by Nature. Emergence, Complexity and Computation, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-67997-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-67997-6_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67996-9

  • Online ISBN: 978-3-319-67997-6

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