]. D. Abramson-1992, J. Abramson, and . Abela, A parallel genetic algorithm for solving the school timetabling problem, Proceedings of the Fifteenth Australian Computer Science Conference (ACSG-15), pp.1-11, 1992.

]. E. Alba and J. M. Troya, A survey of parallel distributed genetic algorithms, Complexity, vol.5, issue.4, pp.31-52, 1999.
DOI : 10.1002/(SICI)1099-0526(199903/04)4:4<31::AID-CPLX5>3.0.CO;2-4

]. A. Albrecht, J. Chauvin, F. A. Lafossas, S. Potteau, and G. Corde, Development of highly premixed combustion Diesel engine model : from simulation to control design. SAE Paper No, p.77, 1072.

G. Varvara, C. Asouti-et-kyriakos, and . Giannakoglou, Aerodynamic optimization using a parallel asynchronous evolutionary algorithm controlled by strongly interacting demes, Engineering Optimization, vol.41, issue.3, pp.241-257, 2009.

]. A. Bethke, Comparison of genetic algorithms and gradient-based optimizers on parallel processors :Efficiency of use of processing capacity, Tech. Rep, issue.197, p.29, 1976.

B. Nicola-beume, M. Naujoks, and . Emmerich, SMS-EMOA: Multiobjective selection based on dominated hypervolume, European Journal of Operational Research, vol.181, issue.3, pp.1653-1669, 2007.
DOI : 10.1016/j.ejor.2006.08.008

]. M. Bolling, H. Pitsch, J. C. Hewson, and K. Seshadri, Reduced n-heptane mechanism for non-premixed combustion with emphasis on pollutant-relevant intermediate species, Symposium (international) on Combustion, pp.729-737, 1996.

]. B. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

]. W. Bossert, Mathematical optimization : Are there abstract limits on natural selection ? Mathematical Challenges to the Neo-Darwinian Interpretation of Evolution, pp.35-46, 1967.

J. Branke, H. Schmeck, and K. Deb, Parallelizing multi-objective evolutionary algorithms: cone separation, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), pp.1952-1957, 2004.
DOI : 10.1109/CEC.2004.1331135

URL : http://repository.ias.ac.in/81667/1/98-a.pdf

]. H. Braun, On solving travelling salesman problems by genetic algorithms, Parallel Problem Solving from Nature (PPSN), pp.129-133, 1990.
DOI : 10.1007/BFb0029743

]. A. Brooker, J. Dennis, P. D. Frank, D. B. Serafini-adn, V. Torczon et al., A rigorous framework for optimization of expensive functions by surrogates, Structural Optimization, vol.16, issue.1, pp.1-13, 1998.
DOI : 10.1007/BF01197708

E. Cantu-paz, Efficient and accurate parallel genetic algorithms, p.29, 2000.
DOI : 10.1007/978-1-4615-4369-5

]. C. Choi and R. D. Reitz, An experimental study on the effects of oxygenated fuel blends and multiple injection strategies on DI diesel engine emissions, Fuel, vol.78, issue.11, pp.78-89, 2002.
DOI : 10.1016/S0016-2361(99)00058-7

]. C. Coello-coello, D. A. Van-veldhuizen, and G. B. Lamont, Evolutionary algorithms for solving multi-objective problems, 2002.
DOI : 10.1007/978-1-4757-5184-0

]. K. Deb, Optimization for engineering design : Algorithms and examples, 1995.

]. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002.
DOI : 10.1109/4235.996017

]. K. Deb, P. Zope, and A. Jain, Distributed Computing of Pareto-Optimal Solutions with Evolutionary Algorithms, Evolutionary Multi-objective Optimization ? EMO'03, pp.534-549, 2003.
DOI : 10.1007/3-540-36970-8_38

]. Deb, M. Mohan, and S. Mishra, Towards a Quick Computation of Well-Spread Pareto-Optimal Solutions, C. Fonseca et al., editeur, EMO'03, pp.222-236, 2003.
DOI : 10.1007/3-540-36970-8_16

]. T. Donateo, D. Laforgia, G. Aloisio, and S. Mocavero, Evolutionary Algorithm as a Tool for Advanced Designing of Diesel Engines, International Journal of Computational Intelligence Research, vol.2, issue.2, pp.169-180, 2006.
DOI : 10.5019/j.ijcir.2006.60

]. J. Durillo, A. J. Nebro, F. Luna, and E. Alba, A study of master-slave approaches to parallelize NSGA-II, 2008 IEEE International Symposium on Parallel and Distributed Processing, pp.1-8, 2008.
DOI : 10.1109/IPDPS.2008.4536375

]. B. Enaux, Simulation aux grandes échelles d'un moteur à allumage commandé -Évaluations des variabilités cycliques, p.76, 2010.

]. L. Fogel, A. J. Owens, and M. J. Walsh, Artificial Intelligence through Simulated Evolution, 1966.
DOI : 10.1109/9780470544600.ch7

]. C. Fonseca and P. J. Fleming, An Overview of Evolutionary Algorithms in Multiobjective Optimization, Evolutionary Computation, vol.3, issue.1, pp.1-16, 1995.
DOI : 10.1162/evco.1994.2.3.221

]. T. Forgarty and R. Huang, Implementing the genetic algorithm on transuter based parallel processing systems, Parallel Problem Solving from Nature (PPSN), pp.145-149, 1991.

]. A. Giunta and L. Watson, A comparison of approximation modeling techniques : Polynomial versus interpolating models. Rapport technique, p.57, 1998.

]. D. Goldberg, B. Korb, and K. Deb, Messy genetic algorithms : Motivation, analysis and first results, Complex Systems, vol.3, issue.11, pp.493-530, 1989.

]. J. Grefenstette, Parallel adaptive algorithms for function optimization Rapport technique, p.29, 1981.

]. P. Grosso, Computer simulations of genetic adaptation : Parallel subcomponent interaction in a multilocus model, p.31, 1985.

L. S. Haimes, D. A. Lasdon, and . Wismer, On a bicriterion formulation of the problems of integrated system identification and system optimization, IEEE Trans. on systems Man of Cybernetics, pp.122-128, 1986.

]. M. Hansen-1998, A. Hansen, and . Jaszkiewicz, Evaluating the quality of approxmations to the non-dominated set. Rapport technique, p.16, 1998.

]. N. Hansen and A. Ostermeier, Completely Derandomized Self-Adaptation in Evolution Strategies, Evolutionary Computation, vol.9, issue.2, pp.159-195, 2001.
DOI : 10.1016/0004-3702(95)00124-7

]. R. Hauser and R. Manner, Implementation of standard genetic algorithm on MIMD machines, Parallel Problem Solving from Nature (PPSN III), pp.504-513, 1994.
DOI : 10.1007/3-540-58484-6_293

M. Liki, J. Kamiura, S. Watanabe, and H. Hiroyasu, Multi-Objective Optimization of Diesel Engine Emissions and Fuel Economy Using Genetic Algorithms and Phenomenological Model, 2002.

]. H. Hiroyasu-2003, H. Hiroyasu, T. Miao, M. Hiroyasu, J. Miki et al., Genetic Algorithms Optimization of Diesel Engine Emissions and Fuel Efficiency with Air Swirl, EGR,Injection Timing and Multiple Injections, SAE Technical Paper Series, p.78, 2003.
DOI : 10.4271/2003-01-1853

]. J. Holland, Adaptation in natural and artificail systems, 1975.

]. C. Igel, N. Hansen, and S. Roth, Covariance Matrix Adaptation for Multi-objective Optimization, Evolutionary Computation, vol.15, issue.1, pp.1-28, 2007.
DOI : 10.1109/TEVC.2003.810758

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

C. Igel, T. Suttorp, and N. Hansen, Steady-state selection and efficient covariance matrix update in MO-CMA-ES, S. Obayashi et al., editeur, EMO'07, pp.171-185, 2007.

]. J. Janicka, W. Kolbe, and W. Kollmann, Closure of the transport equation for the probability density function of turbulent scalar fields, J. Non- Equilib. Thermodyn, vol.4, issue.79, pp.47-66, 1977.

]. J. Knowles, L. Thiele, and E. Zitzler, A Tutorial on the Performance Assessment of Stochastic Multiobjective Optimizers, ETH Zurich, p.16, 2006.

]. J. Koza, Genetic programming as a means for programming computers by natural selection, Statistics and Computing, vol.4, issue.2, p.12, 1992.
DOI : 10.1007/BF00175355

]. J. Koza, Genetic programming ii : Automatic discovery of reusable programs, p.12, 1994.

]. D. Krige, A statistical approach to some basic mine valuation problems on the Witwatersrand, J, of Chem.,Metal. and Mining Soc. of South Africa, pp.119-139, 1951.

]. F. Kursawe, A variant of evolution strategies for vector optimization, Parallel Problem Solving from Nature I (PPSN I), pp.193-197, 1990.
DOI : 10.1007/BFb0029752

]. F. Lafossas, M. Marbaix, and P. Menegazzi, Development and application of a 0D D.I. Diesel cmbustion model for emissions prediction. SAE Paper No, p.77, 1841.

]. M. Laumanns, L. Thiele, K. Deb, and E. Zitzler, Combining Convergence and Diversity in Evolutionary Multiobjective Optimization, Evolutionary Computation, vol.9, issue.3, pp.263-282, 2002.
DOI : 10.1109/4235.797969

]. C. Law, ]. Loshchilov, M. Schoenauer, and M. Sebag, Combustion physics Dominance-Based Pareto-Surrogate for Multi-Objective Optimization, editeur, Simulated Evolution and Learning LNCS, vol.6457, issue.56, pp.78-230, 2006.

]. I. Loshchilov, M. Schoenauer, M. Sebag, M. Ricardo, and H. C. Takahashi, Not all parents are equal for, Proc. EMO'2011, pp.31-45, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00565282

]. A. Lutz, R. Kee, and J. Miller, Senkin : a fortran program for predicting homogeneous gas phase chemical kinetics with sensitivity analysis. Rapport technique, p.81, 1988.

]. G. Matheron, Principles of Geostatics, Economic Geol, pp.1246-1268, 1963.

]. C. Pettey, A massively distributed parallel genetic algorithm (mdpGA) Rapport technique, Tech.Rep.No. CMU-CS-92-196R), p.31, 1992.

]. C. Pettey, Population structures : diffusion (cellular) models. Rapport technique, Handbook of evolutionary computation, C6, vol.44, issue.6, pp.1-6, 1997.

M. Pilát and R. Neruda, ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model, 2011 IEEE Congress of Evolutionary Computation (CEC), pp.1202-1208, 2011.
DOI : 10.1109/CEC.2011.5949753

]. F. Pischinger, H. Schutle, and J. Hansen, The Diesel engine's future, p.75, 1988.

]. J. Schaffer, Some Experiments in Machine Learning Using Vector Evaluated Genetic Algorithms, p.24, 1984.

]. B. Scholkopf and A. J. Smola, Learning with kernels, p.59, 2002.

]. J. Schott, Fault Tolerant Design Using Single and Multi-Criteria Genetic Algorithms, p.16, 1995.

]. P. Senecal, D. T. Montgomery, and R. D. Reitz, A methodology for engine design using multi-dimensional modelling and genetic algorithms with validation through experiments, International Journal of Engine Research, vol.78, issue.3, p.95, 2000.
DOI : 10.1243/1468087001545155

]. B. Silverman, Density estimation for statistics and data analysis, p.18, 1986.
DOI : 10.1007/978-1-4899-3324-9

]. A. Smola and B. Scholkopf, A tutorial on support vector regression, Statistics and Computing, vol.14, issue.3, pp.199-222, 2004.
DOI : 10.1023/B:STCO.0000035301.49549.88

]. N. Bibliographie, K. Srinvas, and . Deb, Multi-Objective function optimization using non-dominated sorting genetic algorithm, Evolutionary Computation, vol.2, pp.221-248, 1994.

]. S. Subramaniam and S. B. Pope, A mixing model for turbulent reactive flows based on Euclidean minimum spanning trees, Combustion and Flame, vol.115, issue.4, pp.270-282, 2004.
DOI : 10.1016/S0010-2180(98)00023-6

]. G. Syswerda, A Study of Reproduction in Generational and Steady-State Genetic Algorithms, Foundations of Genetic Algorithms, pp.94-101, 1991.
DOI : 10.1016/B978-0-08-050684-5.50009-4

]. K. Tan, T. H. Lee, and E. F. Khor, Evolutionary algorithms for multiobjective optimization : performance assessments and comparisons, Artificial Intelligence Review, issue.4, pp.251-290, 2002.

]. T. Tanaka, A. Ando, and K. Ishizaka, Study on pilot injection of DI diesel engine using common-rail injection system, JSAE Review, vol.23, issue.3, pp.297-302, 2002.
DOI : 10.1016/S0389-4304(02)00195-9

]. K. Tsao, Y. Dong, and Y. Xu, Investigation of Flow Field and Fuel Spray in a Direct. Injection Diesel Engine via KIVA-II Program, SAE Technical Paper Series, p.95, 1990.
DOI : 10.4271/901616

]. V. Vapnik, The nature of statistical learning theory, p.59, 1995.

]. D. Veldhuizen, Multiobjective Evolutionary Algorithms : Classifications , analyses, and New Innovations, 1999.

]. I. Voutchkov-2006, A. Voutchkov, and . Keane, Multiobjective Optimization Using surrogates Institute for Peoplecentred Computation, pp.167-175, 2006.

]. J. Weber, N. Peters, A. Pawlowski, R. Kneer, S. H. Tahry et al., Hergart a et A.Lippert. Diesel Spray Characterization Using a Micro- Genetic Algorithm and Optical Measurements, SAE Paper, vol.790833, p.95, 1998.

]. D. Whitley, Cellular genetic algorithms, Proceedings of the Fifth International Conference on Genetic Algorithms, pp.658-689, 1993.

M. Yagoubi, L. Thobois, and M. Schoenauer, An Asynchronous Steadystate NSGA-II Algorithm for Multi-Objective Optimization of Diesel Combustion, H. Rodrigues et al., editeur, Abstract. Proc. 2nd Intl Conf. on Engineering Optimization ? EngOpt'2010, p.77, 2010.

]. M. Yagoubi, L. Thobois, and M. Schoenauer, Asynchronous Evolutionary Multi-Objective Algorithms with heterogeneous evaluation costs, 2011 IEEE Congress of Evolutionary Computation (CEC), pp.21-28, 2011.
DOI : 10.1109/CEC.2011.5949593

URL : https://hal.archives-ouvertes.fr/hal-00625318

]. M. Yagoubi and M. Schoenauer, Asynchronous master/slave moeas and heterogeneous evaluation costs, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference, GECCO '12, p.35, 2012.
DOI : 10.1145/2330163.2330303

URL : https://hal.archives-ouvertes.fr/hal-00689965

]. L. Zhang, T. Ueda, T. Takatsuki, and Y. Yokota, A Study of the Effects of Chamber Geometries on Flame Behavior in a DI Diesel Engine, SAE Technical Paper Series, p.95, 1995.
DOI : 10.4271/952515

]. F. Zhao, T. N. Asmus, D. N. Assanis, J. E. Dec, J. A. Eng et al., Homogeneous charge compression ignition (hcci) engines, p.90, 2003.

]. E. Zitzler and L. Thiele, An Evolutionary Approach for Multiobjective Optimization : The Strength Pareto Approach, TIK Report, vol.43, p.20, 1998.

]. E. Zitzler, Evolutionary Algorithms for Multiobjective Optimization : Methods and Applications, p.16, 1999.

]. E. Zitzler and L. Thiele, Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach, IEEE Transactions on Evolutionary Computation, vol.3, issue.4, pp.257-271, 1999.
DOI : 10.1109/4235.797969

]. E. Zitzler, K. Deb, and L. Thiele, Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation, vol.8, issue.2, pp.173-195, 2000.
DOI : 10.1109/4235.797969

]. E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, V. Grunert et al., Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation, vol.7, issue.2, pp.117-132, 2003.
DOI : 10.1109/TEVC.2003.810758

. Yao, Eckart Zitzler et Simon Künzli Indicator-Based Selection in Multiobjective Search, LNCS, vol.3242, issue.21, pp.832-842, 2004.