Description
Book Synopsis
1 Introduction.- 1.1 Computational Complexity.- 1.2 Survey of Optimization Techniques.- 1.3 Organization of the Book.- 1.4 Exploratory Problems.- 2 Heuristic Search Methods.- 2.1 Graph Search Algorithm.- 2.2 Heuristic Functions.- 2.3 A* Search Algorithm.- 2.4 Exploratory Problems.- 3 Hopfield Neural Networks.- 3.1 Discrete Hopfield Net.- 3.2 Continuous Hopfield Net.- 3.3 Content-Addressable Memory.- 3.4 Combinatorial Optimization.- 3.5 Exploratory Problems.- 4 Simulated Annealing and Stochastic Machines.- 4.1 Statistical Mechanics and The Metropolis Algorithm.- 4.2 Simulated Annealing.- 4.3 Stochastic Machines.- 4.4 Exploratory Problems.- 5 Mean Field Annealing.- 5.1 Mean Field Approximation.- 5.2 Saddle-Point Expansion.- 5.3 Stability.- 5.4 Parameters of the Mean Field Net.- 5.5 Graph Bipartition An Example.- 5.6 Exploratory Problems.- 6 Genetic Algorithms.- 6.1 Simple genetic Operators.- 6.2 An Illustrative Example.- 6.3 Why Do Genetic Algorithms Work?.- 6.4 Other Genetic Operators.
Table of Contents
Preface. 1. Introduction. 2. Heuristic Search Methods. 3. Hopfield-Neural Networks. 4. Simulated Annealing and Stochastic Machines. 5. Mean Field Annealing. 6. Genetic Algorithms. 7. The Traveling Salesman Problem. 8. Telecommunications. 9. Point Pattern Matching. 10. Multiprocessor Scheduling. 11. Job Shop Scheduling. References. Index.
