Foundations of Genetic Programming (Hardcover)
William B. Langdon, Riccardo Poli
- 出版商: Demos Medical Publis
- 出版日期: 2002-02-14
- 售價: $1,050
- 貴賓價: 9.8 折 $1,029
- 語言: 英文
- 頁數: 260
- 裝訂: Hardcover
- ISBN: 3540424512
- ISBN-13: 9783540424512
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商品描述
Genetic programming (GP), one of the most advanced forms of evolutionary computation, has been highly successful as a technique for getting computers to automatically solve problems without having to tell them explicitly how. Since its inceptions more than ten years ago, GP has been used to solve practical problems in a variety of application fields. Along with this ad-hoc engineering approaches interest increased in how and why GP works. This book provides a coherent consolidation of recent work on the theoretical foundations of GP. A concise introduction to GP and genetic algorithms (GA) is followed by a discussion of fitness landscapes and other theoretical approaches to natural and artificial evolution. Having surveyed early approaches to GP theory it presents new exact schema analysis, showing that it applies to GP as well as to the simpler GAs. New results on the potentially infinite number of possible programs are followed by two chapters applying these new techniques.
Contents
1. Introduction 2. Fitness Landscapes 3. Program Component Schema Theories 4. Pessimistic GP Schema Theories 5. Exact GP Schema Theorems 6. Lessons from the GP Schema Theory 7. The Genetic Programming Search Space 8. The GP Search Space: Theoretical Analysis 9. Example I: The Artificial Ant 10. Exemple II: The Max Problem 11. Genetic Programming Convergence and Bloat 12. Conclusions
商品描述(中文翻譯)
遺傳程式設計(GP)是進化計算中最先進的形式之一,作為一種無需明確告訴電腦如何解決問題的技術,它取得了極大的成功。自十多年前開始,GP已被應用於各種應用領域的實際問題解決。隨著對此類工程方法的興趣增加,人們對GP的工作原理和原因也越來越感興趣。本書將最近有關GP理論基礎的研究整合在一起,提供了一個有條理的概述。首先簡要介紹GP和遺傳演算法(GA),然後討論適應度景觀和其他理論方法,以探討自然和人工演化。在回顧早期的GP理論方法後,本書提出了新的精確模式分析,顯示它不僅適用於GP,也適用於更簡單的GA。接著介紹了關於可能無限數量的程式的新結果,並在兩章中應用了這些新技術。
目錄:
1. 簡介
2. 適應度景觀
3. 程式組件模式理論
4. 悲觀的GP模式理論
5. 精確的GP模式定理
6. 從GP模式理論中獲得的教訓
7. 遺傳程式設計的搜索空間
8. GP搜索空間:理論分析
9. 實例一:人工螞蟻
10. 實例二:最大問題
11. 遺傳程式設計的收斂和膨脹
12. 結論