An Introduction to Genetic Algorithms (Paperback)
暫譯: 遺傳演算法入門 (平裝本)
Melanie Mitchell
- 出版商: MIT
- 出版日期: 1998-03-02
- 售價: $1,920
- 貴賓價: 9.5 折 $1,824
- 語言: 英文
- 頁數: 221
- 裝訂: Paperback
- ISBN: 0262631857
- ISBN-13: 9780262631853
-
相關分類:
Algorithms-data-structures
已絕版
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相關主題
商品描述
Description
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.
Table of Contents
Preface
Acknowledgments
Genetic Algorithms: An Overview
1.1 A Brief History of Evolutionary Computation
1.2 The Appeal of Evolution
1.3 Biological Terminology
1.4 Search Spaces and Fitness Landscapes
1.5 Elements Of Genetic Algorithms
1.6 A Simple Genetic Algorithm
1.7 Genetic Algorithms and Traditional Search Methods
1.8 Some Applications of Genetic Algorithms
1.9 Two Brief Examples
1.10 How Do Genetic Algorithms Work?
Genetic Algorithms in Problem Solving
2.1 Evolving Computer Programs
2.2 Data Analysis and Prediction
2.3 Evolving Neural Networks
Genetic Algorithms in Scientific Models
3.1 Modeling Interactions Between Learning And Evolution
3.2 Modeling Sexual Selection
3.3 Modeling Ecosystems
3.4 Measuring Evolutionary Activity
Theoretical Foundations of Genetic Algorithms
4.1 Schemas and the Two-Armed Bandit Problem
4.2 Royal Roads
4.3 Exact Mathematical Models Of Simple Genetic Algorithms
4.4 Statistical-Mechanics Approaches
Implementing a Genetic Algorithm
5.1 When Should a Genetic Algorithm Be Used?
5.2 Encoding a Problem for a Genetic Algorithm
5.3 Adapting the Encoding
5.4 Selection Methods
5.5 Genetic Operators
5.6 Parameters for Genetic Algorithms
Conclusions and Future Directions
Incorporating Ecological Interactions
Incorporating New Ideas from Genetics
Incorporating Development and Learning
Adapting Encodings and Using Encodings That Permit Hierarchy and Open-Endedness
Adapting Parameters
Connections with the Mathematical Genetics Literature
Extension of Statistical Mechanics Approaches
Identifying and Overcoming Impediments to the Success of GAs
Understanding the Role of Schemas in GAs
Understanding the Role of Crossover
Theory of GAs With Endogenous Fitness
Appendix A Selected General References
Appendix B Other Resources
Selected Journals Publishing Work on Genetic Algorithms
Selected Annual or Biannual Conferences Including Work on Genetic Algorithms
Internet Mailing Lists, World Wide Web Sites, and News Groups with Information and Discussions on Ge...
Bibliography
Index
商品描述(中文翻譯)
**描述**
遺傳演算法在科學和工程中被用作適應性演算法,以解決實際問題,並作為自然進化系統的計算模型。本書提供了一個簡短且易於理解的介紹,描述了該領域中一些最有趣的研究,並使讀者能夠自行實現和實驗遺傳演算法。它深入探討了一小組重要且有趣的主題,特別是在機器學習、科學建模和人工生命方面,並回顧了廣泛的研究,包括Mitchell及其同事的工作。應用和建模項目的描述超越了計算機科學的嚴格界限,涵蓋了動態系統理論、博弈論、分子生物學、生態學、進化生物學和種群遺傳學。
**目錄**
前言
致謝
遺傳演算法:概述
1.1 進化計算的簡史
1.2 進化的吸引力
1.3 生物學術語
1.4 搜索空間和適應度景觀
1.5 遺傳演算法的元素
1.6 一個簡單的遺傳演算法
1.7 遺傳演算法與傳統搜索方法
1.8 遺傳演算法的一些應用
1.9 兩個簡短的例子
1.10 遺傳演算法如何運作?
遺傳演算法在問題解決中的應用
2.1 演化計算機程式
2.2 數據分析與預測
2.3 演化神經網絡
遺傳演算法在科學模型中的應用
3.1 建模學習與進化之間的互動
3.2 建模性選擇
3.3 建模生態系統
3.4 測量進化活動
遺傳演算法的理論基礎
4.1 模式與雙臂賭徒問題
4.2 皇家道路
4.3 簡單遺傳演算法的精確數學模型
4.4 統計力學方法
實現遺傳演算法
5.1 何時應使用遺傳演算法?
5.2 為遺傳演算法編碼問題
5.3 調整編碼
5.4 選擇方法
5.5 遺傳運算子
5.6 遺傳演算法的參數
結論與未來方向
整合生態互動
整合來自遺傳學的新想法
整合發展與學習
調整編碼並使用允許層次結構和開放性編碼
調整參數
與數學遺傳學文獻的聯繫
擴展統計力學方法
識別並克服遺傳演算法成功的障礙
理解模式在遺傳演算法中的角色
理解交叉的角色
內生適應度的遺傳演算法理論
附錄A 選定的一般參考文獻
附錄B 其他資源
選定的期刊發表遺傳演算法的研究
選定的年度或雙年度會議,包括遺傳演算法的研究
互聯網郵件列表、網站和新聞組,提供有關遺傳演算法的信息和討論
參考文獻
索引