D. Thierens and D. Goldberg, Convergence models of GA selection schemes, IC. on Parallel Problem Solving from Nature, pp.119-129, 1994.

J. H. Holland, Adaptation in Natural and Artificial Systems, 1975.

D. B. Fogel, Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 1995.

D. E. Goldberg and K. Deb, A comparative analysis of selection schemes used in genetic algorithms, Foundations of genetic algorithms, vol.1, pp.69-93, 1991.

M. Mitchell, An introduction to genetic algorithms, 1998.

N. Mahammed and S. M. Benslimane, Toward Multi Criteria Optimization of Business Processes Design, International Conference on Model and Data Engineering-MEDI 2016, pp.98-107, 2016.

Y. Zhou and Y. Chen, The Methodology for Business Process Optimized Design, IECON Proceedings (Industrial Electronics Conference, vol.2, pp.1819-1824, 2003.

I. Hofacker and R. Vetschera, Algorithmical approaches to business process design, Computers & Operations Research, vol.28, issue.13, pp.1253-1275, 2001.

K. Vergidis, A. Tiwari, B. Majeed, and R. Roy, Optimisation Of Business Process Designs: An Algorithmic Approach With Multiple Objectives, International Journal of Production Economics, vol.109, issue.1, pp.105-121, 2007.

K. Vergidis, D. Saxena, and A. Tiwari, An Evolutionary Multi-Objective Framework For Business Process Optimisation, Applied Soft Comp, vol.12, issue.8, pp.2638-2653, 2012.

K. Vergidis, C. Turner, A. Alechnovic, and A. Tiwari, An Automated Optimisation Framework For The Development Of Re-Configurable Business Processes: A Web Services Approach, Int J Comp Integ M, vol.28, issue.1, pp.41-58, 2015.

K. Georgoulakos, K. Vergidis, G. Tsakalidis, and N. Samaras, Evolutionary MultiObjective Optimization of business process designs with pre-processing, Evolutionary Computation (CEC, pp.897-904, 2017.

M. Wibig, Dynamic Programming and Genetic Algorithm for Business Processes Optimisation, Intern. Journal of Intelligent Systems and Applications, vol.5, issue.1, p.44, 2012.

S. T. Farsani, M. Aboutalebi, and H. Motameni, Customizing NSGAII to Optimize Business Processes Designs, Research Journal of Recent Sciences, vol.2, pp.74-79, 2013.

H. Ishibuchi and Y. Shibata, A similarity-based mating scheme for evolutionary multi objective optimization, Genetic and Evolutionary Computation Conference, pp.1065-1076, 2003.

M. Emmerich, N. Beume, and B. Naujoks, An EMO algorithm using the hyper volume measure as selection criterion, International Conference on Evolutionary MultiCriterion Optimization, pp.62-76, 2005.

A. Trivedi, N. M. Pindoriya, and D. Srinivasan, Modified NSGA-II for day-ahead multi-objective thermal generation scheduling, IPEC, 2010, pp.752-757, 2010.

D. H. Phan and J. Suzuki, Boosting indicator-based selection operators for evolutionary multi objective optimization algorithms, 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp.276-281, 2011.

X. Zhong, Y. Zhao, and Q. Han, An Improved Non dominated Sorting Multi objective Genetic Algorithm and Its Application, 2015.

N. Mahammed and S. M. Benslimane, An Evolutionary Algorithm Based Approach for Business Process Multi-Criteria Optimization, International Journal of Organizational and Collective Intelligence (IJOCI), vol.7, issue.2, pp.34-53, 2017.

K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, IC on Parallel Problem Solving From Nature, pp.849-858, 2000.

J. F. Crow and M. Kimura, Efficiency of truncation selection, Proceedings of the National Academy of Sciences, vol.76, issue.1, pp.396-399, 1979.

H. Mühlenbein and H. M. Voigt, Gene pool recombination in genetic algorithms, Meta-Heuristics, pp.53-62, 1996.

G. Leroy, E. Verrier, J. C. Meriaux, and X. Rognon, Genetic diversity of dog breeds: within-breed diversity comparing genealogical and molecular data, Animal Genetics, vol.40, issue.3, pp.323-332, 2009.

D. V. Liyanage, Identification of genotypes of coconut palms suitable for breeding, Experimental Agriculture, vol.3, issue.03, pp.205-210, 1967.

S. Magnussen and C. W. Yeatman, Predictions of genetic gain from various selection methods in open pollinated Pinus banksiana progeny trials, Silvae genetica, vol.39, issue.3-4, pp.140-153, 1990.

C. Berthouly, G. Leroy, T. N. Van, H. H. Thanh, B. Bed'hom et al., Genetic analysis of local Vietnamese chickens provides evidence of gene flow from wild to domestic populations, BMC genetics, vol.10, issue.1, p.1, 2009.

N. Mahammed, S. M. Benslimane, and N. Hamdani, An Approach to Addressing a Business Process Multi-Objective Optimization Issue with MA-NSGAII, EDiS'2017, pp.17-18, 2017.

D. Grigori, Business Process Intelligence, Computers in Industry, vol.53, pp.321-343, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00130689