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PISA — A Platform and Programming Language Independent Interface for Search Algorithms

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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 introduces an interface specification (PISA) that allows to separate the problem-specific part of an optimizer from the problem-independent part. We propose a view of the general optimization scenario, where the problem representation together with the variation operators is seen as an integral part of the optimization problem and can hence be easily separated from the selection operators. Both parts are implemented as independent programs, that can be provided as ready-to-use packages and arbitrarily combined. This makes it possible to specify and implement representation-independent selection modules, which form the essence of modern multiobjective optimization algorithms. The variation operators, on the other hand, have to be defined in one module together with the optimization problem, facilitating a customized problem description. Besides the specification, the paper contains a correctness proof for the protocol and measured efficiency results.

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References

  1. M. Emmerich and R. Hosenberg. TEA — a C++ library for the design of evolutionary algorithms. Technical Report CI-106/01, SFB 531, Universität Dortmund, 2000.

    Google Scholar 

  2. M. Laumanns, L. Thiele, E. Zitzler, E. Welzl, and K. Deb. Running time analysis of multi-objective evolutionary algorithms on a simple discrete optimization problem. In Parallel Problem Solving From Nature — PPSN VII, 2002.

    Google Scholar 

  3. K. Tan, T. H. Lee, D. Khoo, and E. Khor. A Multiobjective Evolutionary Algorithm Toolbox for Computer-Aided Multiobjective Optimization. IEEE Transactions on Systems, Man, and Cybernetics—Part B: Cybernetics, 31(4):537–556, August 2001.

    Article  Google Scholar 

  4. L. Thiele, S. Chakraborty, M. Gries, and S. Künzli. Network Processor Design 2002: Design Principles and Practices, chapter Design Space Exploration of Network Processor Architectures. Morgan Kaufmann, 2002.

    Google Scholar 

  5. E. Zitzler, M. Laumanns, and L. Thiele. SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization. In K. Giannakoglou, D. Tsahalis, J. Periaux, K. Papailiou, and T. Fogarty, editors, Evolutionary Methods for Design, Optimisation, and Control, pages 19–26, Barcelona, Spain, 2002. CIMNE.

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© 2003 Springer-Verlag Berlin Heidelberg

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Bleuler, S., Laumanns, M., Thiele, L., Zitzler, E. (2003). PISA — A Platform and Programming Language Independent Interface for Search Algorithms. 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_35

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  • DOI: https://doi.org/10.1007/3-540-36970-8_35

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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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