《基於種群機率模型的最佳化技術》是2010年上海交通大學出版社出版的圖書，作者是姜群。
基本介紹
內容簡介,圖書目錄,
內容簡介
本書較系統地討論了遺傳算法和分布估計算法的基本理論，並在二進制搜尋空間實驗性地比較了幾種分布估算法。在此基礎上深入地論述了構建一類新的分布估計算法的思路和實現方法，最後介紹了分布估計算法在計算機科學、資源管理等領域的一些成功套用實例及分布估計算法的幾種有效改進方法。
圖書目錄
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
