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基于种群概率模型的优化技术：从算法到应用（英文版）
作者：姜群 著
出版社：上海交通大学出版社
出版时间：2010-04-01
ISBN：9787313063694
定价：¥48.00
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内容简介
《基于种群概率模型的优化技术：从算法到应用（英文版）》较系统地讨论了遗传算法和分布估计算法的基本理论，并在二进制搜寻空间实验性地比较了几种分布估算法。在此基础上深入地论述了构建一类新的分布估计算法的思路和实现方法，最后介绍了分布估计算法在计算机科学、资源管理等领域的一些成功应用实例及分布估计算法的几种有效改进方法。
作者简介
暂缺《基于种群概率模型的优化技术：从算法到应用（英文版）》作者简介
目录
Chapter 1 Fundamentals and Literature
1.1 Optimization Problems
1.2 Canonical Genetic Algorithm
1.3 Individual Representations
1.4 Mutation
1.5 Recombination
1.6 Population Models
1.7 Parent Selection
1.8 Survivor Selection
1.9 Summary
Chapter 2 The Probabilistic Model -building Genetic Algorithms
2.1 Introduction
2.2 A Simple Optimization Example
2.3 Different EDA Approaches
2.4 Optimization in Continuous Domains with EDAs
2.5 Summary
Chapter 3 An Empirical Comparison of EDAs in Binary Search Spaces
3.1 Introduction
3.2 Experiments
3.3 Test Functions for the Convergence Reliability
3.4 Experimental Results
3.5 Summary
Chapter 4 Development of a New Type of EDAs Based on Principle of Maximum Entropy
4.1 Introduction
4.2 Entropy and Schemata
4.3 The Idea of the Proposed Algorithms
4.4 How Can the Estimated Distribution be Computed and Sampled?
4.5 New Algorithms
4.6 Empirical Results
4.7 Summary
Chapter 5 Applying Continuous EDAs to Optimization Problems
5.1 Introduction
5.2 Description of the Optimization Problems
5.3 EDAs to Test
5.4 Experimental Description
5.5 Summary
Chapter 6 Optimizing Curriculum Scheduling Problem Using EDA
6.1 Introduction
6.2 Optimization Problem of Curriculum Scheduling
6.3 Methodology
6.4 Experimental Results
6.5 Summary
Chapter 7 Recognizing Human Brain Images Using EDAs
7.1 Introduction
7.2 Graph Matching Problem
7.3 Representing a Matching as a Permutation
7.4 Apply EDAs to Obtain a Permutation that Symbolizes the Solution
7.5 Obtaining a Permutation with Continuous EDAs
7.6 Experimental Results
7.7 Summary
Chapter 8 Optimizing Dynamic Pricing Problem with EDAs and GA
8.1 Introduction
8.2 Dynamic Pricing for Resource Management
8.3 Modeling Dynamic Pricing
8.4 An EA Approaches to Dynamic Pricing
8.5 Experiments and Results
8.6 Summary
Chapter 9 Improvement Techniques of EDAs
9.1 Introduction
9.2 Tradeoffs are Exploited by Efficiency-Improvement Techniques
9.3 Evaluation Relaxation: Designing Adaptive Endogenous Surrogates
9.4 Time Continuation: Mutation in EDAs
9.5 Summary
1.1 Optimization Problems
1.2 Canonical Genetic Algorithm
1.3 Individual Representations
1.4 Mutation
1.5 Recombination
1.6 Population Models
1.7 Parent Selection
1.8 Survivor Selection
1.9 Summary
Chapter 2 The Probabilistic Model -building Genetic Algorithms
2.1 Introduction
2.2 A Simple Optimization Example
2.3 Different EDA Approaches
2.4 Optimization in Continuous Domains with EDAs
2.5 Summary
Chapter 3 An Empirical Comparison of EDAs in Binary Search Spaces
3.1 Introduction
3.2 Experiments
3.3 Test Functions for the Convergence Reliability
3.4 Experimental Results
3.5 Summary
Chapter 4 Development of a New Type of EDAs Based on Principle of Maximum Entropy
4.1 Introduction
4.2 Entropy and Schemata
4.3 The Idea of the Proposed Algorithms
4.4 How Can the Estimated Distribution be Computed and Sampled?
4.5 New Algorithms
4.6 Empirical Results
4.7 Summary
Chapter 5 Applying Continuous EDAs to Optimization Problems
5.1 Introduction
5.2 Description of the Optimization Problems
5.3 EDAs to Test
5.4 Experimental Description
5.5 Summary
Chapter 6 Optimizing Curriculum Scheduling Problem Using EDA
6.1 Introduction
6.2 Optimization Problem of Curriculum Scheduling
6.3 Methodology
6.4 Experimental Results
6.5 Summary
Chapter 7 Recognizing Human Brain Images Using EDAs
7.1 Introduction
7.2 Graph Matching Problem
7.3 Representing a Matching as a Permutation
7.4 Apply EDAs to Obtain a Permutation that Symbolizes the Solution
7.5 Obtaining a Permutation with Continuous EDAs
7.6 Experimental Results
7.7 Summary
Chapter 8 Optimizing Dynamic Pricing Problem with EDAs and GA
8.1 Introduction
8.2 Dynamic Pricing for Resource Management
8.3 Modeling Dynamic Pricing
8.4 An EA Approaches to Dynamic Pricing
8.5 Experiments and Results
8.6 Summary
Chapter 9 Improvement Techniques of EDAs
9.1 Introduction
9.2 Tradeoffs are Exploited by Efficiency-Improvement Techniques
9.3 Evaluation Relaxation: Designing Adaptive Endogenous Surrogates
9.4 Time Continuation: Mutation in EDAs
9.5 Summary
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