Multi-objective evolutionary algorithms MOEAs use apopulation of solutions to approximate the Pareto optimal set in a single run.. Combining decision maker’s preference with directions o
MINISTRY OF EDUCATION AND TRAINING MINISTRY OF NATIONAL DEFENSE MILITARY TECHNICAL ACADEMY NGUYEN LONG A MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM USING DIRECTIONS OF IMPROVEMENT AND APPLICATION THE THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN MATHEMATICS Hanoi – 2014 download by : skknchat@gmail.com MINISTRY OF EDUCATION AND TRAINING MINISTRY OF NATIONAL DEFENSE MILITARY TECHNICAL ACADEMY A MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM USING DIRECTIONS OF IMPROVEMENT AND APPLICATION Specialized in: Fundamentals of Mathematics for Informatics Code: 62 46 01 10 THE THESIS IS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN MATHEMATICS SUPERVISORS: ASSOC PROF DR BUI THU LAM ASSOC PROF DR NGUYEN VAN HAI Hanoi - 2014 download by : skknchat@gmail.com download by : skknchat@gmail.com Abstract A multi-objective optimization problem involves at least two conflicting objectives and it has a set of Pareto optimal solutions Multi-objective evolutionary algorithms (MOEAs) use a population of solutions to approximate the Pareto optimal set in a single run MOEAs have attracted a lot of research attention during the past decade They are still one of the hottest research areas in the field of Computational Intelligence and they are the main focus of this thesis Firstly, the main concepts for multi-objective optimization are presented, then the thesis concerns about mentions the solving multi-objective optimization problems by multi-objective evolutionary algorithms This thesis also conducts a survey on the usage of directorial information in search’s guidance Through the survey, the thesis indicates that there is a need to have more investigation on how to have an e↵ective guidance from both aspects: Automatically guiding the evolutionary process to make the MOEA balanced between exploitation and exploration Combining decision maker’s preference with directions of improvement to guide the MOEAs during optimal process toward the most preferred region in the objective space To address this, the thesis builds up all its proposals based on a direction based multiobjective evolutionary algorithm (DMEA), the most recent one with a systematic way to maintain directions of improvement so some related issues on DMEA are raised and analysed, hypothesised as primary research problems in this thesis At the highlighted chapters, the thesis discusses all the issues on using directions of improvement in DMEA through thesis’s contributions: Design a new proposed direction based multi-objective evolutionary algorithm version ii download by : skknchat@gmail.com II (DMEA-II) with following improvement techniques: • Using an adaptive ratio between convergence and spread directions • Using a Ray based density niching method for the main population • Using a new Ray based density selection scheme for dominated solutions selection • Using a new parents selection scheme for the o↵springs perturbation In order to validate the proposed algorithm, a series of experiments on a wide range of test problems was conducted It obtained quite good results on primary performance metrics, including the generation distance (GD), the inverse generation distance (IGD), the hypervolume (HYP) and the two set coverage (SC) The analysis on the results indicates the better performance of DMEA-II in comparison with the most popular MOEAs Proposes an interactive method for DMEA-II as the second aspect of having an e↵ective guidance An interactive method is introduced with three ray based approaches: Rays Replacement, Rays Redistribution, Value Added Niching The experiments carried out a case study on several test problems and showed quite good results Introduces a SpamAssassin based Spam Email Detection System that uses DMEAII The proposed system helps users to have more good choices for the SpamAssassin system in configuration iii download by : skknchat@gmail.com Acknowledgements The first of all, I would like to express my respectful thanks to my principal supervisor, Assoc.Prof Bui Thu Lam for his directly guidance to my PhD progress Assoc.Prof Bui has given me knowledge and passion as the motivation of this thesis His valued guidance has inspired much of the research in the thesis I also wish to thank my co-supportive Assoc.Prof Nguyen Van Hai for his suggestions and knowledge during my research, especially the relation between theories and real problems in work I also would like to thank Prof Hussein Abbass, Assoc.Prof Tran Quang Anh and Assoc.Prof Dao Thanh Tinh for their invaluable support throughout my PhD I feel lucky to work with such excellent people I also would like to thank all of my fellows in the Department of Software Technology and Evolutionary Computation research group for their assistance and support Last but not least, I also would like to acknowledge the support of my family, especially my parents Dr Nguyen Nghi, Truong Thi Hong, they worked hard and believed strongly in their children I also would like to thanks my wife, sisters, brothers who always support me during my research iv download by : skknchat@gmail.com Originality Statement I hereby declare that this thesis is my own work, with my knowledge and belief the thesis has no material previously published or written by others Any contributions made to the research by colleagues, with people in our research team at Le Quy Don Technical University or elsewhere, during my candidature is clearly acknowledged I also declare that the intellectual content in this submission is the research results of my own work, except to the extent that assistance from others in conception or in style, presentation and linguistic expression is acknowledged v download by : skknchat@gmail.com Contents Abstract ii List of Figures ix List of Tables xi Abbreviations xii Introduction 1.1 Overview 1.2 Research Perspectives 1.3 Motivation 1.4 Questions and Hypothesises 1.5 Thesis organization 1.6 Original Contributions 10 Background concepts and Issues 2.1 2.2 13 Common concepts 13 2.1.1 Multi-objective problems 13 2.1.2 Notations 14 2.1.3 General Definitions 14 2.1.4 Pareto Optimality 2.1.5 Weak Pareto Optimality 17 2.1.6 Dominance 17 15 Conventional methods 18 vi download by : skknchat@gmail.com 2.3 2.2.1 No-preference methods 19 2.2.2 A priori methods 19 2.2.3 A posteriori methods 20 2.2.4 Interactive methods 23 An overview of Multi-objective Evolutionary Algorithms 25 2.3.1 Non-elitist methods 25 2.3.2 Elitist methods 26 2.3.3 Performance measures 27 2.3.4 Test problems 29 2.4 Statistical testing 30 2.5 Search’s guidance in MOEAs 31 2.6 2.7 2.5.1 Technique of using guided directions 32 2.5.2 Advantages and disadvantages 45 Research Issues 48 2.6.1 Direction based multi-objective evolutionary algorithm (DMEA) 48 2.6.2 Issue 01: The disadvantages of the fixed ratio between types of directions 51 2.6.3 Issue 02: Lack of an efficient niching method for the main population 52 2.6.4 Issue 03: The disadvantages of using the weighted sum scheme 53 2.6.5 Issue 04: Using a ’hard’ niching method 53 2.6.6 Issue 05: Investigating on how the DM can interact with DMEA 53 Summary 54 A guided methodology using directions of improvement 55 3.1 Using an adaptive ratio between convergence and spread directions 55 3.2 Using a Ray based density niching for the main population 56 3.3 Using a ray based density selection schemes 59 3.4 Direction based Multi-objective Evolutionary Algorithm-II 60 3.4.1 General structure 60 3.4.2 Computational complexity 62 3.4.3 Experimental Studies 62 vii download by : skknchat@gmail.com 3.4.4 Results and Discussion 68 3.5 Analyzing e↵ects of di↵erent selection schemes for the perturbation 81 3.6 Summary 86 A guided methodology using interaction with decision makers 87 4.1 Overview 87 4.2 A multi-point Interactive method for DMEA-II 92 4.3 4.2.1 Rays replacement 93 4.2.2 Rays Redistribution 94 4.2.3 Value Added Niching 96 4.2.4 Experimental Studies 97 4.2.5 Results and Discussion 98 Summary 102 An application of DMEA-II for a spam email detection system 104 5.1 Overview 104 5.2 Spam email detection 107 5.3 5.2.1 SpamAssassin 107 5.2.2 Methodology 108 5.2.3 An interactive method 113 5.2.4 Computational complexity 113 5.2.5 Experimental Studies 114 5.2.6 Results and Discussion 115 Summary 123 Conclusions and Future Work 124 6.1 Conclusions 124 6.2 Future directions 129 Publications 130 Appendix A Benchmark sets 132 viii download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [9] D Balasubramanian, Murali C Krishna, and R Murugesan Multi-objective ga- optimized interpolation kernels for reconstruction of high resolution emr images from low-sampled k-space data International Journal of Computational Intelligence and Applications, 8(02):127–140, 2009 [10] Vitor Basto-Fernandes, Iryna Yevseyeva, and Jos´e R M´endez Optimization of antispam systems with multiobjective evolutionary algorithms Inf Resour Manage J., 26(1):54–67, January 2000 [11] P.A.N Bosman and D Thierens Multi-objective optimization with the naive midea In J.A Lozano et al, editor, Towards a New Evolutionary Computation Advances in Estimation of Distribution Algorithms, pages 123–157 Springer-Verlag, Berlin, 2006 [12] Peter A N Bosman and Edwin D de Jong Exploiting gradient information in numerical multi–objective evolutionary optimization In Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, GECCO ’05, pages 755–762, New York, NY, USA, 2005 ACM [13] Martin Brown and Robert E Smith E↵ective use of directional information in multi-objective evolutionary computation In Genetic and Evolutionary Computation (GECCO 2003), pages 778–789 Springer, 2003 [14] Lam T Bui, Hussein A Abbass, and Daryl Essam Local models-an approach to distributed multi-objective optimization Computational Optimization and Applications, 42(1):105–139, 2009 [15] Lam T Bui, Hussein A Abbass, and Daryl Essam Localization for solving noisy multiobjective optimization problems Evolutionary computation, 17(3):379–409, 2009 [16] Lam T Bui and Sameer Alam Multi-Objective Optimization in Computation Intelligence: Theory and Practice Information Science Reference IGI Global, 2008 [17] Lam Thu Bui Advances in Multi-objective evolutionary algorithms People army publishing house, 2013 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 144 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [18] Lam Thu Bui, Kalyanmoy Deb, Hussein A Abbass, and Daryl Essam Interleaving guidance in evolutionary multi-objective optimization Journal of Computer Science and Technology, 23(1):44–63, 2008 [19] Lam Thu Bui, Jing Liu, Axel Bender, Michael Barlow, Slawomir Wesolkowski, and Hussein A Abbass Dmea: a direction-based multiobjective evolutionary algorithm Memetic Computing, pages 271–285, 2011 [20] K Piromsopa C Na Songkhla Statistical rules for thai spam detection Proceeding of: Future Networks, pages 178–184, 2010 [21] Oscar Castillo, Leonardo Trujillo, and Patricia Melin Multiple objective genetic algorithms for path-planning optimization in autonomous mobile robots Soft Computing, 11(3):269–279, 2007 [22] Pei-Chann Chang, Jih-Chang Hsieh, and Chih-Yuan Wang Adaptive multi-objective genetic algorithms for scheduling of drilling operation in printed circuit board industry Appl Soft Comput., 7(3):800–806, June 2007 [23] Fan Yang Chung Kwan and Che Chang A di↵erential evolution variant of nsga ii for real world multiobjective optimization Proceeding ACAL’07 Proceedings of the 3rd Australian conference on Progress in artificial life, pages 345–356, 2007 [24] C A.C Coello, G T Pulido, and M S Lechuga Handling multiple objectives with particle swarm optimization Trans Evol Comp, 8(3):256–279, June 2004 [25] C.A Coello, D.A Van Veldhuizen, and G.B Lamont Evolutionary Algorithms for Solving Multi-Objective Problems Kluwer Academic publishers, New York, 2002 [26] K Deb Multiobjective Optimization using Evolutionary Algorithms John Wiley and Son Ltd, New York, 2001 [27] K Deb and T Goel Controlled elitist non-dominated sorting genetic algorithm for better convergence In Evolutionary Multi-Criteria Optimization, volume 1993 of Lecture Notes in Computer Science, pages 67–81 Springer, 2001 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 145 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [28] K Deb and A Kumar Interactive evolutionary multi-objective optimization and decision-making using reference direction method In GECCO ’07, pages 781–788, 2007 [29] K Deb, A Pratap, S Agarwal, and T Meyarivan A fast and elitist multiobjective genetic algorithm: Nsga-ii Evolutionary Computation, IEEE Transactions on, 6(2):182–197, 2002 [30] K Deb and J Sundar Reference point based multi-objective optimization using evolutionary algorithms In GECCO ’06: Proceedings of the 8th annual conference on Genetic and Evolutionary Computation, pages 635–642, New York, NY, USA, 2006 ACM Press [31] K Deb, L Thiele, M Laumanns, and E Zitzler Scalable test problems for evolutionary multi-objective optimization, TIK-Report no 112 Technical report, Computer Engineering and Networks Laboratory (TIK), Swiss Federal Institute of Technology (ETH), Zurich, 2001 [32] Satchidananda Dehuri, Srikanta Patnaik, Ashish Ghosh, and Rajib Mall Application of elitist multi-objective genetic algorithm for classification rule generation Applied Soft Computing, 8(1):477–487, 2008 [33] Kenneth Alan DeJong An analysis of the behavior of a class of genetic adaptive systems PhD thesis, University of Michigan, Ann Arbor, 1975 [34] G.Nildem Demir, A.Sima Uyar, and Sule Gunduz-Oguducu Multiobjective evolutionary clustering of web user sessions: a case study in web page recommendation Soft Computing, 14(6):579–597, 2010 [35] R C Eberhart and Y Shi Particle swarm optimization: developments, applications and resources In Proceedings of the Congress on Evolutionary Computation IEEE Press, 2001 [36] Francis Ysidro Edgeworth Mathematical psychics: An essay on the application of mathematics to the moral sciences C Keagann Paul, 1881 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 146 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [37] Ronald Aylmer Fisher Statistical methods for research workers Genesis Publishing Pvt Ltd, 1925 [38] Jorg Fliege and Benar Fux Svaiter Steepest descent methods for multicriteria optimization Mathematical Methods of Operations Research, 51(3):479–494, 2000 [39] C.M Fonseca and P.J Fleming Genetic algorithms for multiobjective optimization: Formulation, discussion and generalization In Proceedings of the Fifth Int Conf on Genetic Algorithms, San Mateo, California, pages 416–423 Morgan Kau↵man Publishers, 1993 [40] Mar´ıa Jos´e Gacto, Rafael Alcal´a, and Francisco Herrera Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems Soft Computing, 13(5):419–436, 2009 [41] Ashish Ghosh and Bhabesh Nath Multi-objective rule mining using genetic algorithms Information Sciences, 163(1):123–133, 2004 [42] Maoguo Gong, Fang Liu, Wei Zhang, Licheng Jiao, and Qingfu Zhang Interactive moea/d for multi-objective decision making In GECCO’ 2011, pages 721–728, 2011 [43] Julia Handl and Joshua Knowles An evolutionary approach to multiobjective clustering Evolutionary Computation, IEEE Transactions on, 11(1):56–76, 2007 [44] Thomas Hanne and Stefan Nickel A multiobjective evolutionary algorithm for scheduling and inspection planning in software development projects European Journal of Operational Research, 167(3):663–678, 2005 [45] Ken Harada and Shigenobu Kobayashi Local search for multiobjective function optimization: Pareto descent method In In The 8th Annual Conference on Genetic and Evolutionary Computation (GECCO-2006), pages 659–666 ACM Press, 2006 [46] Joel Hewlett, Bogdan Wilamowski, and G Dundar Merge of evolutionary computation with gradient based method for optimization problems In Industrial Electronics, 2007 ISIE 2007 IEEE International Symposium on, pages 3304–3309 IEEE, 2007 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 147 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [47] J Horn, N Nafpliotis, and D.E Goldberg A niched pareto genetic algorithm for multiobjective optimization In Proceedings of the First IEEE Conf on Evolutionary Computation, volume 1, pages 82–87 IEEE World Congress on Computational Intelligence, Piscataway, New Jersey, 1994 [48] Juan Gayt´an Iniestra and Javier Garc´ıa Guti´errez Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework Applied Soft Computing, 9(2):512–526, 2009 [49] Antony W Iorio and Xiaodong Li Incorporating directional information within a differential evolution algorithm for multi-objective optimization In Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 691–698 ACM, 2006 [50] Stefan Janson, Daniel Merkle, and Martin Middendorf Molecular docking with multiobjective particle swarm optimization Appl Soft Comput., 8(1):666–675, January 2008 [51] Mehmet Kaya Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules Soft Computing, 10(7):578–586, 2006 [52] Hyoungjin Kim and Meng-Sing Liou Adaptive directional local search strategy for hybrid evolutionary multiobjective optimization Applied Soft Computing, 19(0):290 – 311, 2014 [53] J Knowles and D Corne Approximating the nondominated front using the pareto archived evolution strategy Evol Comp, 8(2):149–172, 2000 [54] J D Knowles and D Corne ”M-PAES: A memetic algorithm for multi-objective optimization” In Proceedings of the Congress on Evolutionary Computation, pages 325–332 IEEE Press, 2000 [55] Tjalling C Koopmans and John Michael Montias On the description and comparison of economic systems Comparison of Economic Systems, 14, 1971 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 148 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [56] Pekka J Korhonen and Jukka Laakso A visual interactive method for solving the multiple criteria problem European Journal of Operational Research, 24(2):277–287, 1986 [57] Adriana Lara, Sergio Alvarado, Shaul Salomon, Gideon Avigad, Carlos A Coello Coello, and Oliver Schă utze The gradient free directed search method as local search within multi-objective evolutionary algorithms In EVOLVE-A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II, pages 153–168 Springer, 2013 [58] Beatrice Lazzerini, Francesco Marcelloni, and Massimo Vecchio A multi-objective evolutionary approach to image quality/compression trade-o↵ in jpeg baseline algorithm Applied Soft Computing, 10(2):548–561, 2010 [59] Loo Hay Lee, Chul Ung Lee, and Yen Ping Tan A multi-objective genetic algorithm for robust flight scheduling using simulation European Journal of Operational Research, 177(3):1948–1968, 2007 [60] Bin-Bin Li and Ling Wang A hybrid quantum-inspired genetic algorithm for multiobjective flow shop scheduling Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 37(3):576–591, June 2007 [61] Hui Li and Qingfu Zhang Comparison between nsga-ii and moea/d on a set of multiobjective optimization problems with complicated pareto sets [62] Hui Li and Qingfu Zhang Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii Evolutionary Computation, IEEE Transactions on, 13(2):284–302, 2009 [63] Hui Li and Qingfu Zhang Multiobjective optimization problems with complicated pareto sets, moea/d and nsga-ii IEEE Trans Evol Comp, pages 284–302, 2009 [64] Ming-Jeng LIN and Ching-Lai HWANG Group decision making under multiple criteria Springer, 1987 [65] Bo Liu, Francisco V Fern´andez, Qingfu Zhang, Murat Pak, Suha Sipahi, and Georges luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 149 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY Gielen An enhanced moea/d-de and its application to multiobjective analog cell sizing In Evolutionary Computation (CEC), 2010 IEEE Congress on, pages 1–7 IEEE, 2010 [66] AG L´opez-Herrera, E Herrera-Viedma, and F Herrera A multiobjective evolutionary algorithm for spam e-mail filtering In Intelligent System and Knowledge Engineering, 2008 ISKE 2008 3rd International Conference on, volume 1, pages 366–371 IEEE, 2008 [67] Samir W Mahfoud Niching methods for genetic algorithms Urbana, 51(95001), 1995 [68] Engin Masazade, Ramesh Rajagopalan, Pramod K Varshney, Chilukuri K Mohan, Gullu Kiziltas Sendur, and Mehmet Keskinoz A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor networks Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 40(2):444– 457, 2010 [69] K Miettinen Nonlinear Multiobjective Optimization Kluwer Academic Publishers, Boston, USA, 1999 [70] Yu Chen Minzhong Liu, Xiufen Zou and Zhijian Wu Performance assessment of dmoeadd with cec 2009 moea competition test instances Proceeding CEC’09 Proceedings of the 11th conference on Congress on Evol Comp, pages 2913–2918, 2009 [71] F Jiang V.Q Tran M.T Vu, Q.A Tran Multilingual rules for spam detection Proceedings of the 7th International Conference on Broadband and Biomedical Communications (IB2COM 2012), pages 106–110, 2012 [72] Anirban Mukhopadhyay and Ujjwal Maulik A multiobjective approach to mr brain image segmentation Applied Soft Computing, 11(1):872–880, 2011 [73] Long Nguyen and Lam Thu Bui A decomposition-based interactive method for multiobjective evolutionary algorithms The Journal on Information Technologies and Communications (JITC), E-2(5(9)):17–24, 2012 [74] Long Nguyen and Lam Thu Bui A multi-point interactive method for multi-objective luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 150 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY evolutionary algorithms In The fourth International Conference on Knowledge and Systems Engineering (KSE 2012), Da Nang, Vietnam, 2012 [75] Long Nguyen and Lam Thu Bui A new selection strategy for the direction-based multi-objective evolutionary algorithm Journal of Development and Application on Information and Telecommunication Technology (JITC), E-2(6(10)):35–48, 2013 [76] Long Nguyen and Lam Thu Bui The e↵ects of di↵erent selection schemes on the direction based multi-objective evolutionary algorithm In The first NAFOSTED Conference on Information and Computer Science 2014 (NICS’14), Ha Noi, Vietnam, March 2014 [77] Long Nguyen, Lam Thu Bui, and Hussein Abbass A new niching method for the direction-based multi-objective evolutionary algorithm In 2013 IEEE Symposium Series on Computational Intelligence, Singapore, 2013 [78] Long Nguyen, Lam Thu Bui, and Hussein Abbass DMEA-II: the direction-based multi-objective evolutionary algorithm-II Soft Computing, 18(11):2119–2134, 2014 [79] Long Nguyen, Lam Thu Bui, and Tran Quang Anh Toward an interactive method for dmea-ii and application to the spam-email detection system VNU Journal of Computer Science and Communication Engineering, 30(4):29–44, 2014 [80] Long Nguyen and LamThu Bui A ray based interactive method for direction based multi-objective evolutionary algorithm In Van Nam Huynh, Thierry Denoeux, Dang Hung Tran, Anh Cuong Le, and Son Bao Pham, editors, Knowledge and Systems Engineering, volume 245 of Advances in Intelligent Systems and Computing, pages 173–184 Springer International Publishing, 2014 [81] SN Omkar, J Senthilnath, Rahul Khandelwal, G Narayana Naik, and S Gopalakrishnan Artificial bee colony (abc) for multi-objective design optimization of composite structures Applied Soft Computing, 11(1):489–499, 2011 ¨ ur, Tunga G¨ [82] Levent Ozg¨ ung¨or, and Fikret S Gă urgen Adaptive anti-spam filtering for agglutinative languages: a special case for turkish Pattern Recognition Letters, 25(16):1819–1831, 2004 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 151 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [83] Siddharth Pal, Swagatam Das, and Aniruddha Basak Design of time-modulated linear arrays with a multi-objective optimization approach Progress In Electromagnetics Research B, 23:83–107, 2010 [84] Marco A Panduro, Carlos A Brizuela, David Covarrubias, and Claudio Lopez A tradeo↵ curve computation for linear antenna arrays using an evolutionary multi-objective approach Soft Computing, 10(2):125–131, 2006 [85] Jos´e Maria A Pangilinan and Gerrit K Janssens Evolutionary algorithms for the multiobjective shortest path problem Enformatika, 19, 2007 [86] Vilfredo Pareto Cours d’economie politique Librairie Droz, 1964 [87] Eskelinen Petri and Miettinen Kaisa Trade-o↵ analysis approach for interactive nonlinear multiobjective optimization In OR Spectrum, pages 1–14, 2011 [88] Huyen N.T.M Phuong L.H, Azim Roussanaly, and Ho Tuong Vinh A hybrid approach to word segmentation of vietnamese texts 5196:240–249, 2008 [89] Ken Price, Rainer Storn, and Jouni Lampinen Di↵erential Evolution - A Practical Approach to Global Optimization Springer, Berlin, Germany, 2005 [90] S.Z Zhao P.N Suganthan W Liu S Tiwari Q Zhang, A Zhou Multiobjective optimization test instances for the cec 2009 special session and competition [91] X Li Q.A Tran, H Duan Real-time statistical rules for spam detection Proceedings of the International Journal of Computer Science and Network Security, pages 178–184, 2006 [92] Bin Qian, Ling Wang, De-Xian Huang, and Xiong Wang Multi-objective no-wait flow-shop scheduling with a memetic algorithm based on di↵erential evolution Soft Computing, 13(8-9):847–869, 2009 [93] Gang Quan, Garrison W Greenwood, Donglin Liu, and Sharon Hu Searching for multiobjective preventive maintenance schedules: Combining preferences with evolutionary algorithms European Journal of Operational Research, 177(3):1969–1984, 2007 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 152 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [94] Daniel Radu and Yvon Besanger A multi-objective genetic algorithm approach to optimal allocation of multi-type facts devices for power systems security In Power Engineering Society General Meeting, 2006 IEEE, pages 8–pp IEEE, 2006 [95] A.R Rahimi-Vahed, S.M Mirghorbani, and M Rabbani A new particle swarm algorithm for a multi-objective mixed-model assembly line sequencing problem Soft Computing, 11(10):997–1012, 2007 [96] Margarita Reyes-Sierra and CA Coello Coello Multi-objective particle swarm optimizers: A survey of the state-of-the-art International journal of computational intelligence research, 2(3):287–308, 2006 [97] AlanP Reynolds and Beatriz Iglesia A multi-objective grasp for partial classification Soft Computing, 13(3):227–243, 2009 [98] Isabel Espirito Santo Roman Denysiuk, Lino Costa Ddmoa: Descent directions based multiobjective algorithm In Proceedings of the Conference on Computational and Mathematical Methods in Science and Engineering (CMMSE 12), pages 460–471 IEEE, 2012 [99] Isabel Espirito Santo Roman Denysiuk, Lino Costa Ddmoa2: Improved descent directions-based multiobjective algorithm In 13th International Conference on Computational and Mathematical Methods in Science and Engineering IEEE, 2013 [100] Rocıo C Romero-Zaliz, Cristina Rubio-Escudero, J Perren Cobb, Francisco Herrera, Oscar Cord´on, and Igor Zwir A multiobjective evolutionary conceptual clustering methodology for gene annotation within structural databases: a case of study on the gene ontology database Evolutionary Computation, IEEE Transactions on, 12(6):679– 701, 2008 [101] RP Runyon, A Haber, DJ Pittenger, and KA Coleman Statistical inference: categorical variables Fundamentals of Behavioral Statistics McGraw Hill, New York, pages 592– 594, 1996 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 153 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [102] Mohammad Saadatseresht, Ali Mansourian, and Mohammad Taleai Evacuation planning using multiobjective evolutionary optimization approach European Journal of Operational Research, 198(1):305–314, 2009 [103] Luciano Sanchez, Jose Otero, and Ines Couso Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms Soft Computing, 13(5):467–479, 2009 [104] R Saravanan, S Ramabalan, N Ebenezer, and C Dharmaraja Evolutionary multi criteria design optimization of robot grippers Applied Soft Computing, 9(1):159–172, 2009 [105] Ruhul Sarker and Tapabrata Ray An improved evolutionary algorithm for solving multi-objective crop planning models Comput Electron Agric., 68(2):191–199, October 2009 [106] Stefan Schăaer, Reinhart Schultz, and Klaus Weinzierl Stochastic method for the solution of unconstrained vector optimization problems Journal of Optimization Theory and Applications, 114(1):209–222, 2002 [107] O Schutze, A Lara, and Carlos A Coello Coello On the influence of the number of objectives on the hardness of a multiobjective optimization problem Evolutionary Computation, IEEE Transactions on, 15(4):444–455, Aug 2011 [108] Soo-Yong Shin, In-Hee Lee, Dongmin Kim, and Byoung-Tak Zhang Multiobjective evolutionary optimization of dna sequences for reliable dna computing Evolutionary Computation, IEEE Transactions on, 9(2):143–158, 2005 [109] Pradyumn Kumar Shukla On gradient based local search methods in unconstrained evolutionary multi-objective optimization In Evolutionary Multi-Criterion Optimization, pages 96–110 Springer, 2007 [110] Valceres VR Silva, Peter J Fleming, Jungiro Sugimoto, and Ryuichi Yokoyama Multiobjective optimization using variable complexity modelling for control system design Applied Soft Computing, 8(1):392–401, 2008 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 154 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [111] Christine Solnon and Khaled Gh´edira Ant colony optimization for multi-objective optimization problems Internation Journal on computer science, 2010 [112] James C Spall Implementation of the simultaneous perturbation algorithm for stochastic optimization Aerospace and Electronic Systems, IEEE Transactions on, 34(3):817– 823, 1998 [113] Wolfram Stadler Multicriteria Optimization in Engineering and in the Sciences, volume 37 Springer, 1988 [114] Rainer Storn and Kenneth Price Di↵erential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, 1995 [115] Reza Tavakkoli-Moghaddam, Alireza Rahimi-Vahed, and Ali Hossein Mirzaei A hybrid multi-objective immune algorithm for a flow shop scheduling problem with biobjectives: Weighted mean completion time and weighted mean tardiness Information Sciences, 177(22):5072 – 5090, 2007 [116] G Timmel Ein stochastisches suchverrahren zur bestimmung der optimalen kompromilsungen bei statischen polzkriteriellen optimierungsaufgaben Wiss Z TH Ilmenau, 6(1):5, 1980 [117] Long Nguyen Anh Quang Tran and Lam Thu Bui Dmea-ii and its application on spam email detection problems In Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA 2014), Ha Noi, Vietnam [118] Steve Uhlig A multiple-objectives evolutionary perspective to interdomain traffic engineering International Journal of Computational Intelligence and Applications, 5(02):215–230, 2005 [119] D.A.V Veldhuizen Multiobjective Evolutionary Algorithms: Classifications, Analyses, and New Innovation PhD thesis, Department of Electrical Engineering and Computer Engineering, Airforce Institue of Technology, Ohio, 1999 [120] Minh Tuan Vu, Quang Anh Tran, Quang Minh Ha, and Lam Thu Bui A multi-objective luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 155 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY approach for vietnamese spam detection In Knowledge and Systems Engineering, pages 211–221 Springer, 2014 [121] MinhTuan Vu, QuangAnh Tran, QuangMinh Ha, and LamThu Bui A multi-objective approach for vietnamese spam detection In Processding: Knowledge and Systems Engineering 2013, pages 211–221, 2013 [122] Antony Waldock and David Corne Multiple objective optimisation applied to route planning In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pages 1827–1834 ACM, 2011 [123] Klaus Weinert, Andreas Zabel, Petra Kersting, Thomas Michelitsch, and Tobias Wagner On the use of problem-specific candidate generators for the hybrid optimization of multi-objective production engineering problems Evolutionary computation, 17(4):527–544, 2009 [124] Stefan Wiegand, Christian Igel, and Uwe Handmann Evolutionary multi-objective optimisation of neural networks for face detection International Journal of Computational Intelligence and Applications, 4(03):237–253, 2004 [125] A Wierzbicki The use of reference objectives in multiobjective optimization In G Fandel and T Gal, editors, Multiple Objective Decision Making, Theory and Application, pages 468–486, Berlin and New York, 1980 Springer [126] Li-Ning Xing, Ying-Wu Chen, and Ke-Wei Yang Multi-objective flexible job shop schedule: Design and evaluation by simulation modeling Appl Soft Comput., 9(1):362– 376, January 2009 [127] Iryna Yevseyeva, Vitor Basto-Fernandes, and Jos´e R M´endez Survey on anti-spam single and multi-objective optimization In ENTERprise Information Systems, pages 120–129 Springer, 2011 [128] Iryna Yevseyeva, Vitor Basto-Fernandes, David Ruano-Ord´as, and Jos´e R M´endez Optimising anti-spam filters with evolutionary algorithms Expert Systems with Applications, 2013 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 156 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung BIBLIOGRAPHY [129] Po-Lung Yu A class of solutions for group decision problems Management Science, 19(8):936–946, 1973 [130] Xinjie Yu and Mitsuo Gen Introduction to evolutionary algorithms Springer, 2010 [131] Xianyi Zeng, Yijun Zhu, Ludovic Koehl, Mauricio Camargo, Christian Fonteix, and Fran¸cois Delmotte A fuzzy multi-criteria evaluation method for designing fashion oriented industrial products Soft Computing, 14(12):1277–1285, 2010 [132] Q F Zhang and H Li Moea/d: A multi-objective evolutionary algorithm based on decomposition 2007 [133] Yang Zhang and Peter I Rockett A generic multi-dimensional feature extrac- tion method using multiobjective genetic programming Evolutionary Computation, 17(1):89–115, 2009 [134] Yang Zhang and Peter I Rockett A generic optimising feature extraction method using multiobjective genetic programming Applied Soft Computing, 11(1):1087–1097, 2011 [135] Zhuhong Zhang Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control Applied Soft Computing, 8(2):959–971, 2008 [136] Shuguang Zhao and Licheng Jiao Multi-objective evolutionary design and knowledge discovery of logic circuits based on an adaptive genetic algorithm Genetic Programming and Evolvable Machines, 7(3):195–210, 2006 [137] Stanley Zionts Decision making: Some experiences, myths and observations In Multiple Criteria Decision Making, pages 233–241 Springer, 1997 [138] E Zitzler, M Laumanns, and L Thiele SPEA2: Improving the strength pareto evolutionary algorithm for multiobjective optimization In K C Giannakoglou, D T Tsahalis, J Periaux, K D Papailiou, and T Fogarty, editors, Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pages 95–100 Int CMINE, 2001 luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 157 download by : skknchat@gmail.com luan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dungluan.van.thac.si.nghien.cuu.de.xuat.giai.thuat.tien.hoa.da.muc.tieu.dua.tren.thong.tin.dinh.huong.va.ung.dung 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