1 Introduction
1.1 Graph Algorithms
1.2 Computational Complexities of Algorithms
1.3 Graph Theoretic Definitions and Notations
1.4 Genetic Algorithms
1.4.1 Components of a GA
1.4.2 General structure of a GA
1.5 Theoretical Foundation of GAs
1.5.1 Schemata and building blocks
1.5.2 GAs and traditional search methods
1.6 Arithmetic of Imprecise Numbers
1.6.1 Interval number and interval arithmetic
1.6.2 Triangular fuzzy number and its arithmetic...
2 Maximum Weight Independent Set of a Graph
2.1 Introduction
2.2 0-1 Integer Programming Formulation
2.3 The GA for the MWIS Problem
2.3.1 Genetic representation
2.3.2 Fitness function
2.3.3 Population initialization
2.3.4 Selection
2.3.5 Crossover
2.3.6 Mutation
2.4 Computational Results
2.5 A summary
3 Minimum Weight Dominating Set of a Graph
3.1 Introduction
3.2 0-1 Integer Programming Formulation
3.3 The GA for the MWDS Problem
3.3.1 Genetic representation
3.3.2 Fitness function
3.3.3 Population initialization
3.3.4 Selection
3.3.5 Crossover
3.3.6 Mutation
3.4 The Proposed GA and its Efficiency
3.5 Computational Results
3.6 A Summary
4 p-center and p-radius of a Graph in Crisp and Fuzzy Environments
4.1 Introduction
4.2 Genetic Representations and Operators
4.2.1 Representations
4.2.2 Initialization
4.2.3 Fitness function
4.2.4 Selection
4.2.5 Crossover
4.2.6 Mutation
4.3 Computational Result
4.4 Fuzzy Network and Different Location Models
4.5 Comparison of Imprecise Numbers
本书就是一部由国外原版引进的关于算法的数学专著。遗传算法(GA)是一种启发式算法，它通过使用由自然进化启发而来的技术手段（例如交叉、突变和选择），生成针对优化问题的解决方案.这些解决方案已经成功地被用于数学和工程学的不同领域的连续优化问题中，在本书中，我们选择了一些图论中的NP一接近问题，例如优选权重独立集问题、优选权重控制集问题、p一中心问题等，这些问题都可以使用遗传算法来解决，带有卷曲边权的网络上的最短路径问题和PERT可以用多项式求解，我们已经证明，边权不准确的网络上的这些问题的时间复杂度是指数级的，本书还给出了解决这些问题的遗传算法。
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