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

By Stefan Bleuler, Marco Laumanns, Lothar Thiele, Eckart Zitzler. International Conference on Evolutionary Multi-Criterion Optimization, 2003

538 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/ab3347a1a818e9a83d891a315edc1cbb1e5eee3d

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Citations in the corpus

COCO: a platform for comparing continuous optimizers in a black-box setting (2016, background): “It provides both multiobjective test problems (discrete and continuous; toy as well as real-world problems) and an extensive set of scripts to assess the solvers’ performance in terms of quality indicator measures such as the hypervolume indicator and various epsilon indicators [13].”
Recent advances in selection hyper-heuristics (2020, background): “…of multiobjective hyper-heuristic studies in the literature which use existing software libraries for multiobjective optimisation, such as, PISA accessible at http://www.tik.ee.ethz.ch/pisa/ (Bleuler et al., 2003) and jMetal accessible at http://jmetal.sourceforge.net/ (Durillo and Nebro, 2011).”
A systematic approach to exploring embedded system architectures at multiple abstraction levels (2006, methodology): “We have implemented the MMPN problem as a PISA module [50] and subsequently used the already available SPEA2 optimizer to solve it.”
Covariance Matrix Adaptation for Multi-objective Optimization (2007, methodology): “We use the performance assessment tools contributed to the PISA (Bleuler et al., 2003) software package with standard parameters.”
ParadisEO: A Framework for the Reusable Design of Parallel and Distributed Metaheuristics (2004, background): “Another and completely different way this separation is performed is adopted in Bleuler et al. (2003).”
MOCell: A cellular genetic algorithm for multiobjective optimization (2009, methodology): “The algorithm is implemented within the framework PISA [5].”
HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization (2011, unknown): “HypE is implemented within the PISA framework (Bleuler et al., 2003) and tested in two versions: the first (HypE) uses fitness-based mating selection as described in Algorithm 5, while the second (HypE*) employs a uniform mating selection scheme where all individuals have the same probability of bei”
The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “There are other tools, writen in C/C++, such as PISA [ 5 ] or Paradiseo-MOEO [6].”
jMetal: A Java framework for multi-objective optimization (2011, background): “However, a drawback of PISA is that their internal design hinders to reuse code.”
An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method (2006, methodology): “Besides the BBV and the knapsack problems, practically all test functions available from the PISA website 1 can be used with it.”
A Tutorial on Evolutionary Multiobjective Optimization (2004, methodology): “In practically relevant applications this overhead is small compared to the intrinsic run-time of the optimization [3].”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, background): “PiSA [27] C gEnETiC AlgoRiThm mulTi-obJECTivE, mAnY-obJECTivE, CombinAToRiAl noRmAl low noRmAl”