Mu’tah University College of Graduate studies Novel Methods for Enhancing the Performance of Genetic Algorithms By Esraa Omar Alkafaween Supervisor: Dr. Ahmad Basheer Hassanat A thesis submitted to the College of Graduate Studies in partial fulfillment of the requirements for the Master's degree in Computer Science in the Department of Information Technology, Mu’tah University. Department of Information Technology Mu’tah University, 2015 اﻵ ارء الواردة في الرسالة الجامعية ﻻ ت عبر بالضرورة عن وجهة نظر جامعة مؤتة I ACKNOWLEDGMENT Allah, the Almighty Who gave me the strength, perception and insight for the completion of this work. My Parents I dedicate this work to my dear father who taught me success, and to my beloved mother who gave me compassion and love. My Supervisor Who conveys the light of his knowledge to others, who guides perplexed questioners to the right answers, who has stood by me every step of the way to completion of this work, every appreciation and respect to Dr. Ahmad Hassanat. Many thanks go also to Marion Cobban for editing this thesis. Esra'a Omar Alkafaween II TABLE OF CONTENTS ACKNOWLEDGMENT..............................................................................I TABLE OF CONTENTS ........................................................................... II LIST OF TABLES .................................................................................... IV LIST OF FIGURES ................................................................................... V LIST OF EQUATIONS .......................................................................... VII ABBREVIATIONS ............................................................................... VIII ABSTRACT IN ENGLISH ...................................................................... IX ABSTRACT IN ARABIC .......................................................................... X Chapter 1. Introduction .......................................................................................... 1 1.1. Simple Genetic Algorithms .............................................................. 1 1.2. GA Parameters ................................................................................. 7 1.3. Thesis Contribution .......................................................................... 7 1.4. Thesis Overview .............................................................................. 7 1.5. Summary .......................................................................................... 8 2. Literature Review ................................................................................. 9 2.1. Genetic algorithm (GA) revisited .................................................... 9 2.1.1Crossover operator .................................................................. 9 2.1.2 Mutation operator ................................................................. 11 2.1.3 Literature review (GA) ........................................................ 14 2.2. Diversity and Multi-population Genetic Algorithm (MPGA) ....... 18 2.2.1 Exploration and Exploitation ............................................... 20 2.2.2 Literature review (MPGA) ................................................... 20 2.3. Parallel Genetic Algorithm (PGA)................................................. 22 2.3.1 Literature review (PGA) ...................................................... 26 2.4. Travelling Salesman Problem (TSP) ............................................. 26 2.4.1 Solving TSP using GA ......................................................... 27 2.5. Summary ........................................................................................ 29 3. Proposed Work for Crossover Operator .......................................... 31 3.1. Design and Methodology ............................................................... 31 3.2. Crossover operator ......................................................................... 31 3.2.1 Cut on worst gene crossover (COWGC) ............................. 31 3.2.2 Cut On Worst L+R Crossover (COWLRGC) ...................... 33 3.2.3 Collision Crossover .............................................................. 34 3.2.4 Multi Crossover Operator Algorithms ................................. 35 3.2.4.1 Select the best crossover (SBC) ............................. 36 3.2.4.2 Select any crossover (SAC) ................................... 36 3.3 Experimental Results and Discussion ............................................. 36 3.3.1 Experiments Set 1 ................................................................ 36 3.3.2. Experiments Set 2 ................................................................ 40 3.4. Summary ...................................................................................... 42 III 4. Proposed Work for Mutation Operator ........................................... 43 4.1 Design and Methodology ................................................................ 43 4.2 Mutation Operator ........................................................................... 43 4.2.1 Worst gene with random gene mutation (WGWRGM) ......... 43 4.2.2 Worst gene with worst gene mutation (WGWWGM) ........... 45 4.2.3 Worst LR gene with random gene mutation (WLRGWRGM) ..................................................................... 46 4.2.4 Worst gene with nearest neighbour mutation (WGWNNM) . 46 4.2.5 Worst gene with the worst around the nearest neighbour mutation (WGWWNNM) ..................................................... 48 4.2.6 Worst gene inserted beside nearest neighbour mutation (WGWWNNM) ...................................................... 48 4.2.7 Random gene inserted beside nearest neighbour mutation (RGIBNNM) ........................................................... 48 4.2.8 Swap worst gene locally mutation (SWGLM) ...................... 48 4.2.9 Insert best random gene before worst gene mutation (IBRGBWGM).......................................................................... 50 4.2.10 Insert best random gene before rand gene mutation (IBRGBRGM) ........................................................................... 51 4.2.11 Multi Mutation Operators Algorithms ................................ 51 4.2.11.1 Select the best mutation algorithm (SBM) ........... 51 4.2.11.2 Select any mutation algorithm (SAM) .................. 52 4.3. Experimental Results and Discussion ............................................ 52 4.3.1 Experiments Set 1 ................................................................. 52 4.3.2 Experiments Set 2 ................................................................. 55 4.4 Summary ......................................................................................... 56 5. Combining the Proposed Strategies .................................................. 57 5.1. Experiments Set 1 .......................................................................... 57 5.2. Experiments Set 2 .......................................................................... 58 5.3.Time complexity of the proposed work .......................................... 60 5.4 Summaary ………………..………………………………………62 6. Conclusions and Future Work ............................................................. 63 6.1. Conclusions .................................................................................... 63 6.2. Future work ................................................................................... 64 References ............................................................................................ 65 IV LIST OF TABLES 3.1 Comparison of crossover methods on converging to optimal solution (population size = 200).......................................................... 41 3.2 Comparison of crossover methods on converging to optimal solution (population size = 100) ......................................................... 41 4.1 Result for eleven problems obtained for 4 mutation operators after 8000 generations (population size=200) .................................... 55 4.2 Result for eleven problems obtained for 4 mutation operators after 8000 generations (population size=100) ...................................... 56 5.1 Comparison of the twelve combined methods .................................... 60 V LIST OF FIGURES 1.1 Flow chart of a typical GA ...................................................................... 2 1.2 Binary encoding example ........................................................................ 2 1.3 Permutation encoding example ............................................................... 3 1.4 Value encoding example ....................................................................... 3 1.5 Tree encoding example ....................................................................... 3 1.6 Roulette Wheel selection......................................................................... 4 1.7 Tournament selection (Tournament size=2) ......................................... 5 1.8 One-Point crossover .............................................................................. 5 1.9 Two-Point crossover ............................................................................. 5 1.10 Overcoming local optima by mutation ............................................... 6 1.11 Example of mutation in a binary string ................................................. 6 2.1 Modified crossover ............................................................................... 10 2.2 Uniform crossover ................................................................................