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A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization

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Evolutionary Multi-Criterion Optimization (EMO 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2632))

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Abstract

This paper proposes a real-coded predator-prey GA for multiobjective optimization (RCPPGA). The model takes its inspiration from the spatial predator-prey dynamics observed in nature. RCPPGA differs itself from previous similar work by placing a specific emphasis on introducing a dynamic spatial structure to the predator-prey population. RCPPGA allows dynamic changes of the prey population size depending on available space and employs a BLX-α crossover operator that encourages a more self-adaptive search. Experiments using two different fitness assignment methods have been carried out, and the results are compared with previous related work. Although RCPPGA does not employ elitism explicitly (such as using an external archive), it has been demonstrated that given a sufficiently large lattice size, RCPPGA can consistently produce and maintain a diverse distribution of nondominated optimal solutions along the Pareto-optimal front even after many generations.

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References

  1. Deb, K.: Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester, UK (2001)

    MATH  Google Scholar 

  2. Zitzler, E. and Thiele, L.: Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach, IEEE Transactions on Evolutionary Computation, 3(4): 257–271, (1999)

    Article  Google Scholar 

  3. Hajela, P. and Lin, C.-Y.: Genetic search strategies in multicriterion optimal design, Structural Optimization, vol. 4. New York: Springer, (1992) 99–107

    Google Scholar 

  4. Horn, J. and Nafpliotis, N.: Multiobjective optimization using niched pareto genetic algorithm, IllGAL Report 93005, Illinois Genetic Algorithm Lab., Univ. Illinois, Urbana-Champaign (1993)

    Google Scholar 

  5. Laumanns, M., Rudolph, G. and Schwefel, H. P.: A Spatial Predator-Prey Approach to Multi-Objective Optimization: A Preliminary Study, In: Eiben, A. E., Schoenauer, M. and Schwefel, H.P (eds.): Proceedings of the Parallel Problem Solving From Nature — PPSN V, Springer-Verlag, Amsterdam, Holland, (1998) 241–249

    Chapter  Google Scholar 

  6. Manderick, B. and Spiessens, P.: Fine-grained parallel genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann (1989) 428–433

    Google Scholar 

  7. Cantu-Paz, E.: A Survey of Parallel Genetic Algorithms. Technical Report IlliGAL 97003, University of Illinois at Urbana-Champaign (1997)

    Google Scholar 

  8. Kirley, M., Li, X., and Green, D.: Investigation of a cellular genetic algorithm that mimics landscape ecology. In: McKay, R., et al. (eds.): Simulated Evolution and Learning — SEAL98, volume 1585 Lecture Notes in Artificial Intelligence, Springer-Verlag (1998) 90–97

    Google Scholar 

  9. Kirley, M. A.: A Cellular Genetic Algorithm with Disturbances: Optimisation Using Dynamic Spatial Interactions. In: Journal of Heuristics. 8(3):321–342 (2002)

    Article  MATH  Google Scholar 

  10. Li, X. and Kirley, M.: The Effects of Varying Population Density in a Fine-grained Parallel Genetic Algorithm. In: The Proceedings of the 2002 Congress on Evolutionary Computation (CEC’02). Vol: 2, 1709–1714 (2002)

    Article  Google Scholar 

  11. Li, X. and Sutherland, S.: A Cellular Genetic Algorithm Simulating Predator-Prey Interactions. In: Proceeding of the 4th Asia-Pacific Conference on Simulated Evolution And Learning (SEAL’02), edited by Wang, L., Tan, K.C., Furuhashi, T., Kim, J-H and Yao, X., (2002) 76–80

    Google Scholar 

  12. Smith, R.L. and Smith, T.M.: Elements of Ecology — fourth edition, The Benjamin/Cummings Publishing Company, Inc., Menlo Park, CA 94025 (1998)

    Google Scholar 

  13. Wright, A.: Genetic Algorithms for Real Parameter Optimization. In: Rawlin, G.J.E. (eds.): Foundations of Genetic Algorithms 1. Morgan Kaufmann, San Mateo (1991) 205–218

    Google Scholar 

  14. Eshelman, L. J. and Schaffer, J.: Realcoded genetic algorithms and interval-schemata. In: Foundation of Genetic Algorithms. (1991) 187–202

    Google Scholar 

  15. Deb, K. and Beyer, H.: Self-Adaptive Genetic Algorithms with Simulated Binary Crossover, Evolutionary Computation 9(2): 197–221, MIT Press (2001)

    Article  Google Scholar 

  16. Knowles, J.D. and Corne, D.W.: Approximating the nondonimated frond using the Pareto Archived Evolution Strategy. Evolutionary Computation, 7(3): 1–26 (2000)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science and Information Technology, RMIT University, GPO Box 2476v, Melbourne, VIC, 3001, Australia

    Xiaodong Li

Authors
  1. Xiaodong Li
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Editor information

Editors and Affiliations

  1. Faculty of Sciences and Technology, Centre for Intelligent Systems, University of the Algarve, Campus de Gambelas, 8005-139, Faro, Portugal

    Carlos M. Fonseca

  2. Department of Automatic Control and Systems Engineering, University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Peter J. Fleming

  3. Department of Electrical Engineering, Computer Engineering and Networks Laboratory, Swiss Federal Institute of Technology, Gloriastraße 35, 8092, Zürich, Switzerland

    Eckart Zitzler  & Lothar Thiele  & 

  4. Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory, Indian Institute of Technology, Kanpur, UP, 208 016, India

    Kalyanmoy Deb

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© 2003 Springer-Verlag Berlin Heidelberg

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Li, X. (2003). A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization. In: Fonseca, C.M., Fleming, P.J., Zitzler, E., Thiele, L., Deb, K. (eds) Evolutionary Multi-Criterion Optimization. EMO 2003. Lecture Notes in Computer Science, vol 2632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36970-8_15

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  • DOI: https://doi.org/10.1007/3-540-36970-8_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-01869-8

  • Online ISBN: 978-3-540-36970-7

  • eBook Packages: Springer Book Archive

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