元胞自动机运用简单的、局部规则的、离散的、自下而上的建模方法，借助计算机可以把系统中各个因素之间的非线性关系转化为可执行的程序，去模拟复杂的、全局的、连续的系统。所以，元胞自动机目前已成为研究动态复杂系统的有效手段，在越来越广泛的学科领域内获得应用。
李学伟、吴今培、李雪岩著的《实用元胞自动机导论(英文版)(精)》首先系统地阐释元胞自动机的思想基础、工作原理、建模方法及复杂性分析，然后探讨元胞自动机与遗传算法、神经网络、智能体Agent的沟通、交互和渗透，最后介绍元胞自动机在经济、城市交通管理和疾病传播领域的典型应用。
本书内容新颖，充分反映了元胞自动机跨学科、交叉性的研究思路，理论性与实用性兼顾。本书可作为信息科学与工程、人工智能、系统工程、复杂性管理、经济管理、交通运输专业的高年级本科、研究生公共选修课的教材，也适合从事相关专业的师生、科研工作者参考。

1 The Conceptual Origin of Cellular Automata
1.1 A Way of Thinking in Complexity: Complex Thinking
1.1.1 Simplicity and the Principle of Simplicity
1.1.2 Complexity and Complex Thinking
1.2 A Computing Mode Originated from Complexity---Computation by Rules
1.2.1 The Complexity of Computation
1.2.2 Two Computational Modes for Complexity Research
1.3 A Discrete Dynamics Model Originated from Complexity---Cellular Automata
References
2 The Working Principle of Cellular Automata
2.1 The Invention of Cellular Automata
2.2 The Definition of Cellular Automata
2.2.1 Mathematical Definition
2.2.2 Physical Definition
2.3 The Composition of Cellular Automata
2.3.1 Ceils
2.3.2 Cellular Space
2.3.3 Neighborhood
2.3.4 Rules of Evolution
2.4 Working Process of Cellular Automata
2.5 Information Processing in Cellular Automata
2.6 Basic Characteristics of Cellular Automata
2.7 Three Classical Types of Cellular Automata
2.7.1 Elementary Cellular Automata by Wolfram
2.7.2 Conway's "Game of Life" .
2.7.3 Langton's "Virtual Ants".
2.8 The Philosophical Implications of Cellular Automata
References
3 Model Building Method of Cellular Automata
3.1 Computer Models
3.2 Models of Cellular Automata
3.2.1 The Abstractness of Cellular Automata Models
3.2.2 The Adaptability of Cellular Automata Models
3.2.3 The Serf-organization of Cellular Automata Models
3.2.4 The Configuration of a Cellular Automaton Model
3.3 The Modeling Process of Cellular Automata
3.3.1 Determining the Characteristics of the System Being Studied
3.3.2 Lattice Division of a System
3.3.3 Determining Initial States of the Cells
3.3.4 Determining the Evolution Rules of a System
3.4 Simulation of the Evolution of Investment Strategies in Stock Market
References
4 The Complexity of Cellular Automata
4.1 The Complexity in Nature and Formal Languages
4.1.1 Coarse-Grained Description and Symbol Set
4.1.2 Formal Language and Grammatical Complexity
4.1.3 Formal Languages and the Equivalence Theory of Automata
4.1.4 General-Purpose Computation of Cellular Automata and the Turing Machine
4.2 The Complexity of Cellular Automata's Evolutionary
Configuration 4.2.1 The Complexity of Cellular Automata's Languages
4.2.2 Proof of the Complexity Levels of Several Types of Cellular Automata
4.3 Measurement of the Complexity of Cellular Automata
4.3.1 Entropy and Information Entropy
4.3.2 Cellular Automata and Entropy
4.3.3 The Measure of the Complexity of Cellular Automata's Evolutionary Behavior
4.4 Summary
References
5 Cellular Genetic Algorithms
5.1 Traditional Genetic Algorithms
5.1.1 Overview and Basic Ideas of Genetic Algorithms
5.1.2 The Computational Process of the Traditional Genetic Algorithm
5.1.3 The Performance and Short Comings of the Traditional Genetic Algorithm
5.1.4 Summary
5.2 Cellular Genetic Algorithm
5.2.1 Cellular Evolutionary Algorithm of the Genetic Algorithm (E-CGA)
5.2.2 Self-adaptive Cellular Genetic Algorithm (SA-CGA)
5.2.3 Self-adaptive Cellular Genetic Algorithm with Evolution Rules Added (ESA-CGA)
5.2.4 Improvement 1: Cellular Genetic Algorithm Based on the Strategy of Elite Preservation
5.2.5 Improvement 2: The Introduction of Cellular Genetic Algorithm with Self-adaptive Crossover Operator
5.2.6 Improvement 3: Three-Dimensional Cellular Genetic Algorithm
5.2.7 Summary
5.3 The Application of Self-adaptive Cellular Genetic Algorithm in a Dynamic Environment
5.3.1 Decision-Making Problems in Securities Investment
5.3.2 Raising the Question
5.3.3 Using Traditional Operational Research Method for Solution
5.3.4 Solution for Self-adaptive Cellular Genetic Algorithm
5.3.5 Summary
Referenc