PISA consists of two parts: PISA is a text-based interface for search algorithms. It splits an optimization process into two modules. One module contains all parts specific to the optimization problem (e.g., evaluation of solutions, problem representation, variation of solutions). The other module contains the parts which are independent of the optimization problem (mainly the selection process). These two modules are implemented as separate programs which communicate through text files. PISA is a library of ready-to-go modules, namely optimization problems (test and benchmark problems), selection modules (evolutionary multi-objective optimizers) and modules for performance assessment.

References in zbMATH (referenced in 49 articles , 1 standard article )

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  1. Redondo, J.L.; Fernández, J.; Ortigosa, P.M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  2. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers (2014)
  3. Comis Da Ronco, Claudio; Ponza, Rita; Benini, Ernesto: Aerodynamic shape optimization in aeronautics: a fast and effective multi-objective approach (2014)
  4. Denysiuk, Roman; Costa, Lino; Santo, Isabel Espírito: Generalized multiobjective evolutionary algorithm guided by descent directions (2014)
  5. Derbel, Bilel; Humeau, Jérémie; Liefooghe, Arnaud; Verel, Sébastien: Distributed localized bi-objective search (2014)
  6. Evtushenko, Yu.G.; Posypkin, M.A.: Method of non-uniform coverages to solve the multicriteria optimization problems with guaranteed accuracy (2014)
  7. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: An efficient multi-objective evolutionary fuzzy system for regression problems (2013)
  8. Kim, Hyoungjin; Liou, Meng-Sing: New fitness sharing approach for multi-objective genetic algorithms (2013)
  9. Mavrotas, George; Florios, Kostas: An improved version of the augmented $\varepsilon$-constraint method (AUGMECON2) for finding the exact Pareto set in multi-objective integer programming problems (2013)
  10. Almeida, Carolina P.; Gonçalves, Richard A.: An experimental analysis of evolutionary heuristics for the biobjective traveling purchaser problem (2012)
  11. Liefooghe, Arnaud; Basseur, Matthieu; Humeau Jérémie; Jourdan, Laetitia; Talbi, El-Ghazali: On optimizing a bi-objective flowshop scheduling problem in an uncertain environment (2012)
  12. Liefooghe, Arnaud; Humeau, Jérémie; Mesmoudi, Salma; Jourdan, Laetitia; Talbi, El-Ghazali: On dominance-based multiobjective local search: design, implementation and experimental analysis on scheduling and traveling salesman problems (2012)
  13. Ochoa, Gabriela; Hyde, Matthew; Curtois, Tim; Vazquez-Rodriguez, Jose A.; Walker, James; Gendreau, Michel; Kendall, Graham; McCollum, Barry; Parkes, Andrew J.; Petrovic, Sanja; Burke, Edmund K.: HyFlex: a benchmark framework for cross-domain heuristic search (2012)
  14. Liefooghe, Arnaud; Jourdan, Laetitia; Talbi, El-Ghazali: A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO (2011)
  15. Soylu, Banu; Ulusoy, Selda Kapan: A preference ordered classification for a multi-objective max-min redundancy allocation problem (2011)
  16. Zhu, Weihang; Yaseen, Ashraf; Li, Yaohang: DEMCMC-GPU: an efficient multi-objective optimization method with GPU acceleration on the Fermi architecture (2011)
  17. Bader, Johannes; Deb, Kalyanmoy; Zitzler, Eckart: Faster hypervolume-based search using Monte Carlo sampling (2010)
  18. Barozzi, Elisabetta; Gonzalez, Eduardo; Massari, Umberto: On the generalized mean curvature (2010)
  19. Cai, Li: A two-tier full-information item factor analysis model with applications (2010)
  20. Coello, Carlos A.Coello; Dhaenens, Clarisse; Jourdan, Laetitia: Multi-objective combinatorial optimization: problematic and context (2010)

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Further publications can be found at: http://www.tik.ee.ethz.ch/sop/publications/