research-article

Paradiseo: from a modular framework for evolutionary computation to the automated design of metaheuristics: 22 years of Paradiseo

Authors:

Arnaud Liefooghe,

Sébastien Verel,

Marc Schoenauer,

Juan J. Merelo,

Alexandre Quemy,

Benjamin Bouvier,

Jan GmysAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1522 - 1530

https://doi.org/10.1145/3449726.3463276

Published: 08 July 2021 Publication History

Abstract

The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms. However, no algorithm clearly dominates all its competitors on all problems. Instead, the underlying variety of landscapes of optimization problems calls for a variety of algorithms to solve them efficiently. It is thus of prior importance to have access to mature and flexible software frameworks which allow for an efficient exploration of the algorithm design space. Such frameworks should be flexible enough to accommodate any kind of metaheuristics, and open enough to connect with higher-level optimization, monitoring and evaluation softwares. This article summarizes the features of the Paradiseo framework, a comprehensive C++ free software which targets the development of modular metaheuristics. Paradiseo provides a highly modular architecture, a large set of components, speed of execution and automated algorithm design features, which are key to modern approaches to metaheuristics development.

References

[1]

Maribel García Arenas, Brad Dolin, Juan Julián Merelo Guervós, Pedro Ángel Castillo Valdivieso, Ignacio Fernández De Viana, and Marc Schoenauer. 2002. JEO: Java Evolving Objects. In GECCO, Vol. 2. 991--994.

[2]

Amine Aziz-Alaoui, Carola Doerr, and Johann Dreo. 2021. Towards Large Scale Automated Algorithm Design by Integrating Modular Benchmarking Frameworks. In Proceedings Companion of the Annual Conference on Genetic and Evolutionary Computation (Lille, France) (GECCO'21). to appear.

Digital Library

[3]

Nacim Belkhir, Johann Dreo, Pierre Savéant, and Marc Schoenauer. 2017. Per instance algorithm configuration of CMA-ES with limited budget. In Proceedings of the Genetic and Evolutionary Computation Conference. 681--688.

Digital Library

[4]

Sébastien Cahon, Nordine Melab, and E.-G. Talbi. 2004. Building with paradisEO reusable parallel and distributed evolutionary algorithms. Parallel Comput. 30, 5-6 (2004), 677--697.

Digital Library

[5]

Sébastien Cahon, Nordine Melab, and E-G Talbi. 2004. Paradiseo: A framework for the reusable design of parallel and distributed metaheuristics. Journal of heuristics 10, 3 (2004), 357--380.

Digital Library

[6]

Sébastien Cahon, Nordine Melab, E-G Talbi, and Marc Schoenauer. 2003. ParaDisEO-based design of parallel and distributed evolutionary algorithms. In International Conference on Artificial Evolution (Evolution Artificielle). Springer, 216--228.

[7]

Sébastien Cahon, E-G Talbi, and Nordine Melab. 2003. PARADISEO: a framework for parallel and distributed biologically inspired heuristics. In Proceedings International Parallel and Distributed Processing Symposium. IEEE, 9--pp.

[8]

C. A. Coello Coello, G. B. Lamont, and D. A. Van Veldhuizen. 2007. Evolutionary Algorithms for Solving Multi-Objective Problems (second ed.). Springer, New York, USA.

[9]

Pierre Collet, Evelyne Lutton, Marc Schoenauer, and Jean Louchet. [n.d.]. Take It EASEA. In Parallel Problem Solving from Nature PPSN VI (Berlin, Heidelberg, 2000) (Lecture Notes in Computer Science), Marc Schoenauer, Kalyanmoy Deb, Günther Rudolph, Xin Yao, Evelyne Lutton, Juan Julian Merelo, and Hans-Paul Schwefel (Eds.). Springer, 891--901.

[10]

Pierre Collet and Marc Schoenauer. [n.d.]. GUIDE: Unifying Evolutionary Engines through a Graphical User Interface. In Artificial Evolution (Berlin, Heidelberg, 2004) (Lecture Notes in Computer Science), Pierre Liardet, Pierre Collet, Cyril Fonlupt, Evelyne Lutton, and Marc Schoenauer (Eds.). Springer, 203--215.

[11]

Bilel Derbel and Sébastien Verel. 2020. Fitness landscape analysis to understand and predict algorithm performance for single- and multi-objective optimization. In GECCO '20: Genetic and Evolutionary Computation Conference, Companion Volume, Cancún, Mexico, July 8-12, 2020, Carlos Artemio Coello Coello (Ed.). ACM, 993--1042.

Digital Library

[12]

Benjamin Doerr, Carola Doerr, and Franziska Ebel. 2013. Lessons from the blackbox: Fast crossover-based genetic algorithms. In Proceedings of the 15th annual conference on Genetic and evolutionary computation. 781--788.

Digital Library

[13]

Carola Doerr, Hao Wang, Furong Ye, Sander van Rijn, and Thomas Bäck. [n.d.]. IOHprofiler: A Benchmarking and Profiling Tool for Iterative Optimization Heuristics. ([n.d.]). http://arxiv.org/abs/1810.05281 arXiv: 1810.05281.

[14]

Erich Gamma, Richard Helm, Ralph E. Johnson, and John Vlissides. 1995. Design patterns: elements of reusable object-oriented software. Addison-Wesley.

Digital Library

[15]

Nikolaus Hansen, Anne Auger, Raymond Ros, Steffen Finck, and Petr Pošík. 2010. Comparing Results of 31 Algorithms from the Black-Box Optimization Benchmarking BBOB-2009. In Proceedings of the 12th Annual Conference Companion on Genetic and Evolutionary Computation (Portland, Oregon, USA) (GECCO '10). Association for Computing Machinery, New York, NY, USA, 1689--1696.

Digital Library

[16]

Nikolaus Hansen, Anne Auger, Raymond Ros, Olaf Mersmann, Tea Tušar, and Dimo Brockhoff. [n.d.]. COCO: a platform for comparing continuous optimizers in a black-box setting. 36, 1 ([n.d.]), 114--144.

[17]

H. Hoos and T. Stützle. 2004. Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco, CA, USA.

[18]

Jérémie Humeau, Arnaud Liefooghe, E-G Talbi, and Sébastien Verel. 2013. ParadisEO-MO: From fitness landscape analysis to efficient local search algorithms. Journal of Heuristics 19, 6 (2013), 881--915.

Digital Library

[19]

Robert Hundt. [n.d.]. Loop Recognition in C++/Java/Go/Scala. In Proceedings of Scala Days 2011 (2011). https://days2011.scala-lang.org/sites/days2011/files/ws3-1-Hundt.pdf

[20]

Frank Hutter, Youssef Hamadi, Holger H. Hoos, and Kevin Leyton-Brown. [n.d.]. Performance Prediction and Automated Tuning of Randomized and Parametric Algorithms. In Principles and Practice of Constraint Programming - CP 2006 (Berlin, Heidelberg, 2006) (Lecture Notes in Computer Science), Frédéric Benhamou (Ed.). Springer, 213--228.

Digital Library

[21]

Maarten Keijzer, Juan J Merelo, Gustavo Romero, and Marc Schoenauer. 2001. Evolving objects: A general purpose evolutionary computation library. In International Conference on Artificial Evolution (Evolution Artificielle). Springer, 231--242.

[22]

Pascal Kerschke, Holger H. Hoos, Frank Neumann, and Heike Trautmann. [n.d.]. Automated Algorithm Selection: Survey and Perspectives. 27, 1 ([n. d.]), 3--45.

Digital Library

[23]

Kevin Leyton-Brown, Eugene Nudelman, and Yoav Shoham. [n.d.]. Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions. In Principles and Practice of Constraint Programming - CP 2002 (Berlin, Heidelberg, 2002) (Lecture Notes in Computer Science), Pascal Van Hentenryck (Ed.). Springer, 556--572.

[24]

Arnaud Liefooghe, Laetitia Jourdan, and El-Ghazali Talbi. 2011. A software framework based on a conceptual unified model for evolutionary multiobjective optimization: ParadisEO-MOEO. European Journal of Operational Research 209, 2 (2011), 104--112.

[25]

Manuel López-Ibáñez, Jérémie Dubois-Lacoste, Leslie Pérez Cáceres, Mauro Birattari, and Thomas Stützle. 2016. The irace package: Iterated racing for automatic algorithm configuration. Operations Research Perspectives 3 (2016), 43--58.

[26]

Ogier Maitre, Frédéric Krüger, Stéphane Querry, Nicolas Lachiche, and Pierre Collet. [n.d.]. EASEA: specification and execution of evolutionary algorithms on GPGPU. 16, 2 ([n.d.]), 261--279.

Digital Library

[27]

Nouredine Melab, Thé Van Luong, Karima Boufaras, and El-Ghazali Talbi. 2013. ParadisEO-MO-GPU: a framework for parallel GPU-based local search meta-heuristics. In Proceedings of the 15th annual conference on Genetic and evolutionary computation. 1189--1196.

Digital Library

[28]

Juan-Julián Merelo-Guervós, Israel Blancas-Álvarez, Pedro A Castillo, Gustavo Romero, Pablo García-Sánchez, Víctor M Rivas, Mario García-Valdez, Amaury Hernández-Águila, and Mario Román. 2017. Ranking Programming Languages for Evolutionary Algorithm Operations. In European Conference on the Applications of Evolutionary Computation. Springer, 689--704.

[29]

Juan-Julián Merelo-Guervós, M. G. Arenas, J. Carpio, P. Castillo, V. M. Rivas, G. Romero, and M. Schoenauer. 2000. Evolving objects. In Proc. JCIS 2000 (Joint Conference on Information Sciences), P. P. Wang (Ed.), Vol. I. 1083--1086. ISBN: 0-9643456-9-2.

[30]

Sergio Nesmachnow, Francisco Luna, and Enrique Alba. 2015. An empirical time analysis of evolutionary algorithms as C programs. Software: Practice and Experience 45, 1 (2015), 111--142.

Digital Library

[31]

I. Rechenberg. [n.d.]. Cybernetic Solution Path of an Experimental Problem. ([n. d.]). https://ci.nii.ac.jp/naid/10000137330/

[32]

David Vandevoorde and Nicolai M. Josuttis. [n.d.]. C++ template: the complete guide. Addison-Wesley.

[33]

S. Wagner, G. Kronberger, A. Beham, M. Kommenda, A. Scheibenpflug, E. Pitzer, S. Vonolfen, M. Kofler, S. Winkler, V. Dorfer, and M. Affenzeller. [n.d.]. Architecture and Design of the HeuristicLab Optimization Environment. Springer International Publishing, 197--261.

[34]

Darrell Whitley, Soraya Rana, and Robert B Heckendorn. 1999. The island model genetic algorithm: On separability, population size and convergence. Journal of computing and information technology 7, 1 (1999), 33--47.

[35]

E. Zitzler, M. Laumanns, and S. Bleuler. [n.d.]. A Tutorial on Evolutionary Multiobjective Optimization. Springer Science & Business Media, Chapter 1, 3--38.

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Index Terms

Paradiseo: from a modular framework for evolutionary computation to the automated design of metaheuristics: 22 years of Paradiseo
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
      1. Randomized search
2. Software and its engineering
  1. Software creation and management
    1. Search-based software engineering
  2. Software notations and tools
    1. Development frameworks and environments
      1. Object oriented frameworks

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cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2021

2047 pages

ISBN:9781450383516

DOI:10.1145/3449726

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

Copyright © 2021 ACM.

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Published: 08 July 2021

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GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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de Winter RBäck TVan Stein NLi XHandl J(2024)Modular Optimization Framework for Mixed Expensive and Inexpensive Real-World ProblemsProceedings of the Genetic and Evolutionary Computation Conference Companion10.1145/3638530.3664133(1715-1723)Online publication date: 14-Jul-2024
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Nikolikj AKostovska AVermetten DDoerr CEftimov T(2024)Quantifying Individual and Joint Module Impact in Modular Optimization Frameworks2024 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC60901.2024.10611779(1-8)Online publication date: 30-Jun-2024
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