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Evolutionary algorithms

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Book cover Towards Evolvable Hardware

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1062))

Abstract

Evolutionary algorithms have been gaining increased attention the past few years because of their versatility and are being successfully applied in several different fields of study. We group under this heading a family of new computing techniques rooted in biological evolution that can be used for solving hard problems. In this chapter we present a survey of genetic algorithms and genetic programming, two important evolutionary techniques. We discuss their parallel implementations and some notable extensions, focusing on their potential applications in the field of evolvable hardware.

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References

  1. A. Thompson, I, Harvey and P. Husbands, Unconstrained Evolution and Hard Consequences, this volume.

    Google Scholar 

  2. H. Kitano, Morphogenesis for Evolvable Systems, this volume.

    Google Scholar 

  3. F. Mondada and D. Floreano, Evolution and Mobile Autonomous Robots, this volume.

    Google Scholar 

  4. G. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989.

    Google Scholar 

  5. Z. Michalewicz, Genetic Algorithms+Data Structures=Evolution Programs, Springer-Verlag, Second Edition, Berlin, 1994.

    Google Scholar 

  6. D.B. Fogel, Evolutionary Computation, IEEE Press, New York, 1995.

    Google Scholar 

  7. H. Mühlenbein, M. Schomish and J. Born, The Parallel Genetic Algorithm as Function Optimizer, Parallel Comput. 17, 619, 1991.

    Google Scholar 

  8. G. Syswerda, Uniform Crossover in Genetic Algorithms, in Proc. of the Third Int. Conf. on Genetic Algorithms, J. D. Schaffer (Editor), Morgan Kauffman, 2–9, 1989.

    Google Scholar 

  9. D.E. Goldberg and J. Richardson, Genetic Algorithms with Sharing for Multimodal Function Optimization, in Proc. of the Second Int. Conf. on Genetic Algorithms, J.J. Grefenstette (Editor), Lawrence Erlbaum Associates, Hillsdale, 41–49, 1987.

    Google Scholar 

  10. X. Yin and N. Germay, A Fast Genetic Algorithm with Sharing Scheme Using Cluster analysis Methods in Multimodal Function Optimization, in Proc. Inter. Conf. Artificial Neural Nets and Genetic Algorithms, Innsbruck, Austria, 450–457, 1993.

    Google Scholar 

  11. W. Spears, Simple Subpopulation Schemes, Proc. of the Evolutionary Programming Conference, 296–307, 1994.

    Google Scholar 

  12. D. Beasley, D.R. Bull and R.R. Martin, A Sequential Niche Technique for Multimodal Function Optimization, Evolutionary Computation, 1, 101–125, 1993.

    Google Scholar 

  13. D.W. Hillis, Co-evolving Parasites Improve Simulated Evolution as an optimization Procedure, In Artificial Life II, C. Langton et al. Editors, Addison-Wesley, 313–324, 1992.

    Google Scholar 

  14. M. Sipper, Co-evolving Non-Uniform Cellular Automata to Perform Computations, Physica D, To appear, 1995.

    Google Scholar 

  15. M. Potter and K. De Jong, A Cooperative Coevolutionary Approach to Function Optimization, in Procs. of the Third Conference on Parallel Problem Solving from Nature, Y. Davidor and H.-P. Schwefel Editors, Lecture Notes in Computer Science Vol. 866, Springer-Verlag, 249–257, 1994.

    Google Scholar 

  16. P. Husbands, An Ecosystem Model for Integrated Production Planning, Intl. Journal of Computer Integrated Manufacturing, 6, 74–86, 1993.

    Google Scholar 

  17. M. Potter and K. De Jong, Evolving Neural Networks with Collaborative Species, Procs. of the 1995 Summer Computer Simulation Conference, The Society for Computer Simulation, Ottawa, Canada, 340–345, 1995.

    Google Scholar 

  18. L. Davis, Handbook of Genetic Algorithms, Van Nostrand, New York, 1991.

    Google Scholar 

  19. J. Koza, Genetic Programming, MIT Press, Cambridge, MA, 1992.

    Google Scholar 

  20. K. Kinnear (Editor), Advances in Genetic Programming, MIT Press, 1994.

    Google Scholar 

  21. H. Iba, H. de Garis and T. Sato, Genetic Programming Using a Minimum Description Length Principle, in [20], 1994.

    Google Scholar 

  22. J.P. Cohoon, S.U. Hegde, W.N. Martin and D. Richards: Punctuated Equilibria: a Parallel genetic Algorithm, in Proc. of the Second Int. Conf. on Genetic Algorithms, J.J Grefenstette (Editor), Lawrence Erlbaum Associates, 148, 1987.

    Google Scholar 

  23. R. Tanese: Parallel genetic Algorithm for a Hypercube, in Proc. of the Second Int. Conf. on Genetic Algorithms, J.J Grefenstette (Editor), Lawrence Erlbaum Associates, 177, 1987.

    Google Scholar 

  24. T. Starkweather, D. Whitley and K. Mathias: Optimization Using Distributed Genetic Algorithms, in Parallel Problem Solving from Nature, Lecture Notes in Computer Science Vol. 496, H.-P. Schwefel and R. Männer (Editors), Springer-Verlag, 176, 1991.

    Google Scholar 

  25. B. Manderick and P. Spiessens: Fine-Grained Parallel Genetic Algorithms, in Proc. of the Third Int. Conf. on Genetic Algorithms, J. D. Schaffer (Editor), Morgan Kauffman, 428, 1989.

    Google Scholar 

  26. M. Tomassini, The Parallel Genetic Cellular Automata: Application to Global Function Optimization, Proceedings of the International Conference on Artificial Neural Networks and Genetic Algorithms, Springer-Verlag, Wien, 385, 1993.

    Google Scholar 

  27. A. Loraschi, A. Tettamanzi, M. Tomassini and P. Verda, Distributed Genetic Algorithms with an Application to Portfolio Selection Problems, in Proceedings of the Int. Conf. on Artificial Neural Nets and Genetic Algorithms, D.W. Pearson, N.C. Steele and R.F. Albrecht (Editors), Springer-Verlag, 384, 1995.

    Google Scholar 

  28. J.R. Koza and D. Andre, Parallel Genetic Programming on a Network of Transputers, Computer Science Department, Stanford University, Technical Report CS-TR-95-1542, 1995.

    Google Scholar 

  29. M. Oussaidene, B. Chopard and M. Tomassini, Learning Trading Models Using a Parallel Genetic Programming System, submitted, 1995.

    Google Scholar 

  30. F. Gruau, Artificial Cellular Development in Optimization and Compilation, this volume.

    Google Scholar 

  31. A. Kapsalis, G.D. Smith and V.J. Rayward-Smith, A Unified Paradigm for Parallel Genetic Algorithms, in Evolutionary Computing, AISB Workshop, T.C. Fogarty (Editor), Lecture Notes in Computer Science 865, Springer-Verlag, 1994.

    Google Scholar 

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Authors and Affiliations

  1. Swiss Scientific Computing Center, Manno

    M. Tomassini

  2. Logic Systems Laboratory, Swiss federal Institute of Technology, CH-1015, Lausanne, Switzerland

    M. Tomassini

Authors
  1. M. Tomassini
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Eduardo Sanchez Marco Tomassini

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© 1996 Springer-Verlag Berlin Heidelberg

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Tomassini, M. (1996). Evolutionary algorithms. In: Sanchez, E., Tomassini, M. (eds) Towards Evolvable Hardware. Lecture Notes in Computer Science, vol 1062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61093-6_2

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  • DOI: https://doi.org/10.1007/3-540-61093-6_2

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61093-9

  • Online ISBN: 978-3-540-49947-3

  • eBook Packages: Springer Book Archive

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