C. Bazgan, H. Hugot, and D. Vanderpooten, Solving efficiently the 0-1 multi-objective knapsack problem, Computers & Operations Research, vol.36, issue.1, pp.260-279, 2009.

M. Chiarandini, Stochastic Local Search Methods for Highly Constrained Combinatorial Optimisation Problems, 2005.

F. Daolio, A. Liefooghe, S. Verel, H. Aguirre, and K. Tanaka, Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes, Evolutionary Computation, vol.25, issue.4, pp.555-585, 2017.

L. Thomas, M. S. Dean, and . Boddy, An Analysis of Time-Dependent Planning, Proceedings of the Seventh AAAI National Conference on Artificial Intelligence (AAAI'88), pp.49-54, 1988.

J. Dubois-lacoste, M. López-ibáñez, and T. Stützle, Anytime Pareto local search, European Journal of Operational Research, vol.243, pp.369-385, 2015.

M. E. , Multicriteria Optimization, 2005.

L. José-rui-figueira, M. Paquete, D. Simões, and . Vanderpooten, Algorithmic improvements on dynamic programming for the bi-objective {0,1} knapsack problem, Computational Optimization and Applications, vol.56, pp.97-111, 2013.

V. Grunert-da-fonseca, C. M. Fonseca, and A. O. Hall, Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function, Evolutionary Multi-Criterion Optimization, pp.213-225, 2001.

H. Holger, T. Hoos, and . Stützle, Stochastic Local Search: Foundations & Applications, 2005.

P. Kerschke, H. Holger, F. Hoos, H. Neumann, and . Trautmann, Automated Algorithm Selection: Survey and Perspectives, Evolutionary Computation, vol.27, issue.1, pp.3-45, 2019.

L. Kotthoff, Algorithm Selection for Combinatorial Search Problems: A Survey, Data Mining and Constraint Programming, pp.149-190, 2016.

K. Leyton-brown, E. Nudelman, G. Andrew, J. Mcfadden, and Y. Shoham, A Portfolio Approach to Algorithm Selection, Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03, pp.1542-1543, 2003.

A. Liefooghe, L. Paquete, M. Simões, and J. R. Figueira, Connectedness and Local Search for Bicriteria Knapsack Problems, Evolutionary Computation in Combinatorial Optimization, pp.48-59, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00575922

M. López-ibáñez, L. Paquete, and T. Stützle, Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization, Experimental Methods for the Analysis of Optimization Algorithms, pp.209-222, 2010.

M. López, -. Ibáñez, and T. Stützle, Automatically improving the anytime behaviour of optimisation algorithms, European Journal of Operational Research, vol.235, pp.569-582, 2014.

L. Paquete, T. Schiavinotto, and T. Stützle, On local optima in multiobjective combinatorial optimization problems, Annals of Operations Research, vol.156, pp.83-97, 2007.

S. Polyakovskiy, M. R. Bonyadi, M. Wagner, Z. Michalewicz, and F. Neumann, A comprehensive benchmark set and heuristics for the traveling thief problem, Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO '14), pp.477-484, 2014.

R. John and . Rice, The Algorithm Selection Problem, Advances in Computers, vol.15, pp.60520-60523, 1976.

M. Boas, H. Santos, L. Henrique-de-campos-merschmann, and G. Vanden-berghe, Optimal decision trees for the algorithm selection problem: integer programming based approaches. International Transactions in Operational Research, 2019.

L. Xu, F. Hutter, H. Holger, K. Hoos, and . Leyton-brown, SATzilla: Portfolio-based Algorithm Selection for SAT, Journal of Artificial Intelligence Research, vol.32, pp.565-606, 2008.

S. Zilberstein, Using Anytime Algorithms in Intelligent Systems, AI Magazine, vol.17, issue.3, pp.73-83, 1996.

E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Ph.D. Dissertation. Swiss Federal Institute of Technology Zurich, 1998.

E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, and V. Fonseca, Performance Assessment of Multiobjective Optimizers: An Analysis and Review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.