Arnaud Liefooghe

Associate Professor, Univ Lille (France)

Member of the CRIStAL research center (UMR 9189, Univ Lille, CNRS, EC Lille)

Member of the Inria Lille - Nord Europe research center

Member of the Dolphin research group

Co-director of the international associated laboratory LIA-MODŌ (France / Japan)

Director of studies of the 2nd-year Master degree MIAGE IPI-NT

arnaud.liefooghe [at]

What's new?

Upcoming events      |  EA 2017  |  EvoCOP 2018  |  GECCO 2018  |  PPSN 2018  |

Open positions          |  engineer in optimization and high performance computing  |

Student projects        |  algorithm-selection  |  meta-models  |  learn-predict-optimize in parallel  |  traveling-thief  |  pickup-&-delivery  |  constraints  |

                                    > We are seeking good and motivated candidates for internships, master / PhD thesis, post-docs... feel free to contact us.

Upcoming events

(Parma, Italy)

deadline: November 1, 2017

(Kyoto, Japan)

deadline: January 30, 2018

Short biography

  Since 2010 Associate Professor (Maître de Conférences) in Computer Science, Univ Lille (FR)
Lecturer, Computer Science DepartmentFaculty of Science and Technology
Member of the Dolphin research group, CRIStAL (UMR CNRS 9189), Inria Lille-Nord Europe
  2010 Post-doctoral ResearcherUniv Coimbra (PT)
ECOS research group, CISUCDepartment of Informatics Engineering
  2006—09 PhD Student in Computer Science, Univ Lille 1 (FR)
Adjunct Professor (ATER, UFR EPU, 2008—10)
Part-time Lecturer (vacataire, UFR IEEA, 2006—08)

Research interests
  • Multi-objective optimization
  • Foundations, design and analysis of exact and stochastic local search heuristic algorithms
  • Decomposition-, dominance-, indicator- and set-based search paradigms and their design principles
  • Analytics-driven and model-assisted autonomous search for cross-domain and any-objective optimization
  • Experimental analysis and fundamental understanding of optimization algorithms, benchmarking and performance assessment, statistical and machine learning data analysis
  • Fitness landscape analysis, feature-based performance prediction, algorithm selection, configuration and adaptation, algorithm portfolio
  • Large-scale and expensive NP-hard problems from combinatorial optimization

Recent scientific and academic activities

Recent publications
  • Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe, Sébastien Verel. Using parallel strategies to speed up Pareto local search. 11th International Conference on Simulated Evolution and Learning (SEAL 2017), Lecture Notes in Computer Science (LNCS), Shenzhen, China, 2017
  • Miyako Sagawa, Hernán Aguirre, Fabio Daolio, Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Kiyoshi Tanaka. Learning variable importance to guide recombination on many-objective optimization. 5th International Conference on Smart Computing and Artificial Intelligence (SCAI 2017), Hamamatsu, Japan, 2017
  • Hugo Monzon, Hernán Aguirre, Sébastien Verel, Arnaud Liefooghe, Bilel Derbel, Kiyoshi Tanaka. Closed state model for understanding the dynamics of MOEAs. Genetic and Evolutionary Computation Conference (GECCO 2017), pp 609-616, Berlin, Germany, 2017
  • Jialong Shi, Qingfu Zhang, Bilel Derbel, Arnaud Liefooghe. A parallel tabu search for the unconstrained binary quadratic programming problem. IEEE Congress on Evolutionary Computation (CEC 2017), pp 557-564, Donostia - San Sebastián, Spain, 2017
  • Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka. Towards landscape-aware automatic algorithm configuration: preliminary experiments on neutral and rugged landscapes. European Conference on Evolutionary Computation in Combinatorial Optimisation (EvoCOP 2017), Lecture Notes in Computer Science (LNCS), vol 10197, pp 215-232, Amsterdam, The Netherlands, 2017
  • Arnaud Liefooghe, Bilel Derbel, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka. A fitness landscape analysis of Pareto local search on bi-objective permutation flowshop scheduling problems. 9th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2017), Lecture Notes in Computer Science (LNCS), vol 10173, pp 422-437, Münster, Germany, 2017
  • Fabio Daolio, Arnaud Liefooghe, Sébastien Verel, Hernán Aguirre, Kiyoshi Tanaka. Problem features vs algorithm performance on rugged multi-objective combinatorial fitness landscapesEvolutionary Computation (to appear)
    more ]

Software and ressources
  • MOEA/D — repository of the state-of-the-art developments on MOEA/D and decomposition-based EMO
  • ParadisEO — a software framework for the design of metaheuristics (MOEO for multi-objective optimization, MO for local search)
  • MOSAL — multi-objective sequence alignment tools
  • MoCObench — benchmark instances for multi-objective combinatorial optimization

  • Director of studies of the MIAGE IPI-NT master degree in computer science (2nd year)
  • Algorithmic information theory and computer programming (BSc)
  • Operations research, optimization and decision making (BSc, MSc)
  • Database and decision support systems (MSc)
  • Object-oriented design and programming (MSc)