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 63 articles , 1 standard article )

Showing results 1 to 20 of 63.
Sorted by year (citations)

1 2 3 4 next

  1. Swan, Jerry; Adriaensen, Steven; Brownlee, Alexander E. I.; Hammond, Kevin; Johnson, Colin G.; Kheiri, Ahmed; Krawiec, Faustyna; Merelo, J. J.; Minku, Leandro L.; Özcan, Ender; Pappa, Gisele L.; García-Sánchez, Pablo; Sörensen, Kenneth; Voß, Stefan; Wagner, Markus; White, David R.: Metaheuristics “In the large” (2022)
  2. Hansen, Nikolaus; Auger, Anne; Ros, Raymond; Mersmann, Olaf; Tušar, Tea; Brockhoff, Dimo: COCO: a platform for comparing continuous optimizers in a black-box setting (2021)
  3. Drake, John H.; Kheiri, Ahmed; Özcan, Ender; Burke, Edmund K.: Recent advances in selection hyper-heuristics (2020)
  4. Benitez-Hidalgo, A.; Nebro, AJ; Garcia-Nieto, J.; Oregi, I.; Del Ser, J.: jMetalPy: a Python Framework for Multi-Objective Optimization with Metaheuristics (2019) arXiv
  5. Gergel, Victor; Kozinov, Evgeny: Efficient multicriterial optimization based on intensive reuse of search information (2018)
  6. Capitanescu, F.; Marvuglia, A.; Benetto, E.; Ahmadi, A.; Tiruta-Barna, L.: Linear programming-based directed local search for expensive multi-objective optimization problems: application to drinking water production plants (2017)
  7. Cotnoir, Christopher M.; Terzić, Balša: Decoupling linear and nonlinear regimes: an evaluation of efficiency for nonlinear multidimensional optimization (2017)
  8. Redondo, J. L.; Fernández, J.; Ortigosa, P. M.: FEMOEA: a fast and efficient multi-objective evolutionary algorithm (2017)
  9. Ye Tian, Ran Cheng, Xingyi Zhang, Yaochu Jin: PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization (2017) arXiv
  10. Martí, Luis; García, Jesús; Berlanga, Antonio; Molina, José M.: MONEDA: scalable multi-objective optimization with a neural network-based estimation of distribution algorithm (2016)
  11. Mavrotas, George; Florios, Kostas; Figueira, José Rui: An improved version of a core based algorithm for the multi-objective multi-dimensional knapsack problem: a computational study and comparison with meta-heuristics (2015)
  12. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers (2014)
  13. Comis Da Ronco, Claudio; Ponza, Rita; Benini, Ernesto: Aerodynamic shape optimization in aeronautics: a fast and effective multi-objective approach (2014)
  14. Denysiuk, Roman; Costa, Lino; Santo, Isabel Espírito: Generalized multiobjective evolutionary algorithm guided by descent directions (2014)
  15. Derbel, Bilel; Humeau, Jérémie; Liefooghe, Arnaud; Verel, Sébastien: Distributed localized bi-objective search (2014)
  16. Evtushenko, Yu. G.; Posypkin, M. A.: Method of non-uniform coverages to solve the multicriteria optimization problems with guaranteed accuracy (2014)
  17. Antonelli, Michela; Ducange, Pietro; Marcelloni, Francesco: An efficient multi-objective evolutionary fuzzy system for regression problems (2013)
  18. Dahmani, Nadia; Krichen, Saoussen; Clautiaux, François; Talbi, El-Ghazali: A comparative study of multi-objective evolutionary algorithms for the bi-objective 2-dimensional vector packing problem (2013)
  19. Frutos, Mariano; Tohmé, Fernando: A multi-objective memetic algorithm for the job-shop scheduling problem (2013)
  20. Humeau, J.; Liefooghe, A.; Talbi, E.-G.; Verel, S.: ParadisEO-MO: from fitness landscape analysis to efficient local search algorithms (2013)

1 2 3 4 next

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