此篇論文提出了一種藉由改變基因演算法的流程來加速基因演算法演化速度的方法。這個方法增加了無性繁殖、競爭以及生命上限幾個機制在基因演算法的存活機制之前。這幾個新增的機制會加速基因演算法的收斂速度。實驗結果顯示這個方法在旅人問題會得到47%的收斂速度提升，並且在著色問題上也有10%的提升。除此之外，因為這個方法是藉由新增加的機制來改善基因演算法的效能，所以相關研究於改進基因演算法內部機制的方法依然可以跟這個方法整合。

This thesis proposes an algorithm to enhance the convergence speed of genetic algorithm by modifying the function flow of a simple GA. Additional operators, such as asexual reproduction, competition, and livability, are added before the survival operation. After adding these three operators to the genetic algorithm, the convergence speed can be increased. Experiments indicate that simulations with the proposed algorithm have a 47% improvement in convergence speed on the traveling salesman problem. As for the graph coloring problem, the proposed algorithm also has a 10% improvement. Also, since these operators are additional parts to the original GA, the algorithm can be further improved by enhancing the operators, such as selection, crossover, and mutation.

Chapter 1 Introduction………………………………………………………1
Chapter 2 Related Work………………………………………………………3
2-1 Genetic Algorithms………………………………………………………3
2-1-1 Representation…………………………………………………………5
2-1-1-1 Representation of the Traveling Salesman Problem…………5
2-1-1-2 Representation of the Graph Coloring Problem………………5
2-1-2 Selection………………………………………………………………6
2-1-2-1 Roulette Wheel Approach…………………………………………6
2-1-2-2 Tournament Approach………………………………………………7
2-1-3 Crossover………………………………………………………………8
2-1-3-1 Partially Mapped Crossover………………………………………8
2-1-3-2 Uniform Crossover…………………………………………………9
2-1-3-3 Greedy Partition Crossover………………………………………11
2-1-4 Mutation…………………………………………………………………13
2-1-4-1 Reciprocal Exchange Mutation……………………………………13
2-1-4-2 Inversion Mutation…………………………………………………13
2-2 Researches on Genetic Algorithm……………………………………14
Chapter 3 The Proposed Algorithm…………………………………………16
3-1 Aging………………………………………………………………………19
3-2 Competition………………………………………………………………20
3-3 Reproduction………………………………………………………………23
3-4 The Parameters Setting Method………………………………………24
3-5 The Complexity Analysis………………………………………………25
Chapter 4 Performance Evaluation…………………………………………26
4-1 Traveling Salesman Problem……………………………………………26
4-2 Graph Coloring Problem…………………………………………………31
Chapter 5 Conclusion…………………………………………………………35

References………………………………………………………………………36

[1] J. H. Holland, Adaptation in Natural and Artificial Systems, First Published by University of Michigan Press, 1992.
[2] M. L. Mauldin, Maintaining Diversity in Genetic Search, Proceedings of the National Conference on Artificial Intelligence AAAI-84, pp.247-250, 1984.
[3] L. A. Rendell, A Doubly Layered Genetic Penetrance Learning System, Proceedings of the Third National Conference on Artificial Intelligence AAAI-83, pp.343-347, 1983.
[4] M. Kavian and R. G. McLenaghan, Application of Genetic Algorithms to the Algebraic Simplification of Tensor Polynomials, Proceedings of the 1997 international symposium on Symbolic and algebraic computation, pp.93-100, 1997.
[5] D. Whitley, The GENITOR algorithm and selection pressure: Why Rank-Based Allocation of Reproductive Trials Is Best, Proceedings of 3rd International Conference on Genetic Algorithms, pp.116-121, 1989.
[6] C. K. Ting, S. T. Li, and C. N. Lee, On the harmonious mating strategy through tabu search, Information Sciences, pp.189-214, 2003.
[7] C. H. Su and C. N. Lee, A Multi-Parent Crossover for Combinatorial Optimization Problems, Master Thesis of National Sun Yat-Sen University, Kaohsiung, Taiwan, 2006.
[8] S. Tsutusi, Multi-Parent Recombination in Genetic Algorithm with Search Space Boundary Extension by Mirroring, Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, pp.428-437, 1998.
[9] A. E. Eiben, Multiparent Recombination in Evolutionary Computing, Advances in Evolutionary Computing: theory and applications, Springer-Verlag New York, 2003.
[10] W. Rand, R. Riolo and J. H. Holland, The Effect of Crossover on the Behavior of the GA in Dynamic Environments, Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp.1289-1296, 2006.
[11] Y. Hong, Q. Ren, and J. Zeng, Adaptive Population Size for Univariate Marginal Distribution Algorithm, Proceedings of the Evolutionary Computation, pp.1396-1402, 2005.
[12] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, 1989.
[13] C. R. Houck, J. A. Joines, and Michael G. Kay, A Genetic Algorithm for Function Optimization: A Matlab Implementation, North Carolina State University, Raleigh, NC, Technical Report, 1995.
[14] D. E. Goldberg and R. Lingle, Alleles, Loci and the Traveling Salesman Problem, Proceedings of the First International Conference on Genetic Algorithms, pp.154-159, 1985.
[15] E. Falkenauer, The Worth of the Uniform, Proceedings of the Evolutionary Computation, pp. -782 Vol. 1, 1999.
[16] P. Galinier and J. K. Hao, Hybrid Evolutionary Algorithms for Graph Coloring, Journal of Combinatorial Optimization, pp.379-397, 1998.
[17] B. Freisleben and P. Merz, New Genetic Local Search Operators for the Traveling Salesman Problem, pp.890-899, Proceedings of the Fourth International Conference on Parallel Problem Solving from Nature, 1996.
[18] J. T. David and P. Sampat, Mathematical Programming in a Hybrid Genetic Algorithm for Steiner Point Problems, Proceedings of the ACM symposium on Applied computing, pp.357-363,, 1995.
[19] S. Tsutusi, Multi-Parent Recombination in Genetic Algorithm with Search Space Boundary Extension by Mirroring, Proceedings of the 5th International Conference on Parallel Problem Solving from Nature, pp. 428-437, 1998.
[20] S. V. Jose and A. B. John, Sexual Selection with Competitive/co-operative Operators for Genetic Algorithms, Proceedings of the IASTED International Conference on Neural Networks and Computational Intelligence, pp.191-196, 2003.
[21] http://elib.zib.de/pub/mp-testdata/tsp/tsplib/tsplib.html