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 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: 1. ASSOC. PROF. DR BUI THU LAM 2. ASSOC. PROF. DR NGUYEN VAN HAI Hanoi - 2014 Abstract Amulti-objectiveoptimizationprobleminvolvesatleasttwoconflictingobjectivesandithas 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 Computation al Intelligence and they are the main focus of th i s thesis. Firstly, the main concepts for multi-objective optimization are presented, then the thesis con- cerns about mentions the solving multi-objective optimization problems by multi-objective evolutionary algorithms. This thesis also conducts a sur vey on the usage of directorial infor- mation 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 asp ects: 1. Automati ca l l y guiding the evolutionary process to make the MOEA balanced between exploitation and exploration. 2. Combining decisi on 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 multi- objective evolutionary algorithm (DMEA), the most recent one with a systematic way to maintain directions of impr ovement so some related issues on DMEA are raised and anal- ysed, hypothesised as primary research problems in this thesis. At the highlighted chapters, the thesis discusses all the is su es on using directions of improve- ment in DMEA through thesis’s contributions: 1. Design a new proposed direction based multi-objective evolutionary alg ori t h m version ii II (DMEA-II) with following improvement techniques: • Using an ada p t i ve ratio between convergence and spread directions. • Using a Ray based density niching method for the main populatio n . • Using a new Ray based density selection scheme for dominated solutions selection. • Using a new pare nts 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 perfor m a n ce of DMEA-II in comparison with the most popul a r MOEAs. 2. Propo ses 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 Red i st r i b u t i on , Value Added Niching. The experiments carried out acasestudyonseveraltestproblemsandshowedquitegoodresults. 3. Introdu ces a SpamAssassin based Spam Email Detection System that uses DMEA- II. The pr o posed system helps use rs to have m or e good choices for the Sp a m Assa ssi n system in configuration. iii Acknowledgeme nts The first of all, I would like to express my r espectful thanks to my principal sup er vi sor , Assoc.Prof. Bui Thu Lam for his directly guid a n ce 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 b etween theories and real problems in work. I a l so 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. IalsowouldliketothankallofmyfellowsintheDepartmentofSoftwareTechnologyand Evolutionary Computation research group for their assistance and support. Last but not least, I also would like to acknowledge the supp ort of my family, especially my parents Dr. Nguyen Nghi, Truong Thi Hong, they worked hard an d believed strongly in their children. I also would like to thanks my wife, sisters, brothers who always support me during my research. iv Originality Statement Iherebydeclarethatthisthesisismyownwork,withmyknowledgeandbeliefthethesis has no material previously publish ed or written by others. Any contributions made to the research by colleagues, with people in our research team at Le Qu y Don Technical University or elsewhere, during my candidature is clearly acknowledged. Ialsodeclarethattheintellectualcontentinthissubmissionistheresearchresultsofmyown work, except to the extent that assistance from others in conception or in style, presentation and linguistic expression is acknowledged. v Contents Abstract ii List of Figures ix List of Tables xi Abbreviations xii 1 Introduction 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.4 Questions and Hypothesises . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.5 Thesis organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.6 Original Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Background concepts and Issues 13 2.1 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 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.1.5 Weak Pareto Optimality . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.6 Dominance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Conventional methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 vi 2.2.1 No-preference metho ds . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.2 A priori metho ds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.3 A p osteriori methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.4 Interactive methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3 An overview of Multi-objective Evolutionary Algorithms . . . . . . . . . . . . 25 2.3.1 Non-elitist metho ds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 2.5.1 Technique of using guided directions . . . . . . . . . . . . . . . . . . . 32 2.5.2 Advantages and disadvantages . . . . . . . . . . . . . . . . . . . . . . . 45 2.6 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 metho d 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 2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3 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 po p u l a ti o n . . . . . . . . . . . 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 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 4 A guided methodology using interaction with decision makers 87 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.2 A multi-point Interactive method for DMEA- II . . . . . . . . . . . . . . . . . 92 4.2.1 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 4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 5 An application of DMEA-II for a spam email detection system 104 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.2 Spam email detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 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 5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 6 Conclusions and Future Work 124 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 6.2 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 Publications 130 Appendix A Benchmark sets 132 viii [...]... BẢNG THUẬT NGỮ SỬ DỤNG TRONG LUẬN ÁN Tiếng Anh Tiếng Việt Evolutionary Algorithm Giải thuật tiến hóa Multi-objective Optimization Problem Bài toán tối ưu đa mục tiêu Multi-objective Evolutionary Algorithm Giải thuật tiến hóa Pareto Optimal Front Lớp tối ưu Pareto Pareto Optimal Set Tập tối ưu Pareto Directions of Improvement Hướng cải thiện Convergence Direction Hướng hội tụ Spread Direction Hướng. .. hội tụ Spread Direction Hướng tản mát Differential Direction Hướng vi phân Gradient Direction Hướng Gradient Generational Distance Khoảng cách thế hệ Inverse Generational Distance Khoảng cách thế hệ đảo Hypervolume Siêu diện tích Spam Detection Rate Tỷ lệ nhận dạng thư rác False Alarm Rate Tỷ lệ nhận dạng sai Decision Maker Người ra quyết định Reference point Điểm tham chiếu Reference region Vùng tham... priori methods: This category denotes the process of introducing and incorporating the DM preferences before the search process • A posteriori methods: This category denotes the process of incorporating the preferences at the end of the search process • Interactive methods: This category denotes the process of introducing, incorporating and modifying the DM preferences in an interactive way at any time during... for the Value Added Niching approach with 100 rules 122 x List of Tables 3.1 The main features of test problems 66 3.2 Common parameter settings 67 3.3 Parameters settings 67 3.4 The average values of GD, IGD and HYP 3.5 The average value of GD 74 3.6 The average value of IGD... most approaches are [69]: • Lexicographic order : In order, sorting the objective functions according to their importance, the objectives are optimized in sequence beginning with the most important and finishing with the less important The performance of the algorithm is highly dependent on this ordering given by the DM • Linear aggregating functions: The results of the di↵erent objective functions... real-valued vectors • Evolutionary Programming (EP): derived from the simulation of adaptive behavior in evolution (phenotypic evolution), currently evolutionary programming is a wide evolutionary computing dialect with no fixed representation • Genetic Programming (GP): based on genetic algorithms, but individuals are programs (represented as trees) Recently, researchers extended EA’s paradigms to Di↵erential... find an optimal solution for an optimization problem In EAs, niching methods are used to allow EAs to maintain a diverse population of individuals EAs that incorporate niching methods are capable of locating multiple, optimal solutions within a single population E↵ective niching methods are critical to success of EAs in classification and machine learning, multi-modal optimization, multi-objective optimization,... search algorithm needs to address the exploration and exploitation of a search space Exploration is the process of finding entirely new regions of a search space, whilst exploitation is the process of visiting those regions of a search space within the neighborhood of previously visited points In order to be a successful search algorithm needs to establish a good ratio between exploration and exploitation... have some advantages: This algorithms can be used to solve complex di↵erentiable MOPs, gradient based directions are used so it makes multi-objective algorithms to be good convergence rate, when incorporating with evolution strategy in a hybridization MOEA, the algorithms can have a good convergence rate and avoid the local optimums during the search However, there are some di culties in using gradient... decision makers’ preferred region and also to avoid being trapped too easily in a basin surrounding a local optimum? In order to answer this question, this thesis gives some hypothesises: • When incorporating evolutionary techniques with directions of improvement, those techniques have again the e↵ect on the balance between exploitation and exploration of the algorithms There is a need to have a guidance . anal- ysed, hypothesised as primary research problems in this thesis. At the highlighted chapters, the thesis discusses all the is su es on using directions of improve- ment in DMEA through thesis s. multi-objective evolutionary algorithms. This thesis also conducts a sur vey on the usage of directorial infor- mation in search’s guidance. Through the survey, the thesis indicates that there is a need. Intelligence and they are the main focus of th i s thesis. Firstly, the main concepts for multi-objective optimization are presented, then the thesis con- cerns about mentions the solving multi-objective