An introduction to genetic algorithms
An introduction to genetic algorithms
复制标题
DOI：
10.7551/mitpress/3927.001.0001
复制
发表时间：
1996
期刊：
影响因子：
--
通讯作者：
Melanie Mitchell
中科院分区：
文献类型：
--
作者：
Melanie Mitchell
来自出版商：“这是迄今为止关于遗传算法的最好的通用书籍。它涵盖了背景、历史和动机；它选择了重要的、信息丰富的应用实例，并讨论了遗传算法在科学模型中的应用；它很好地说明了遗传算法理论的现状。最重要的是，这本书以清晰、直接、愉快的散文呈现了它的材料，任何具有大学水平科学背景的人都能读懂。如果你想对遗传算法有一个广泛而扎实的了解——它们从哪里来，用它们做了什么，它们将走向何方——这本书是你的必读之书。——John H. Holland，密歇根大学计算机科学与工程教授、心理学教授；圣达菲研究所的外聘教授。遗传算法在科学和工程中被用作解决实际问题的自适应算法和自然进化系统的计算模型。这篇简短易懂的介绍介绍了该领域一些最有趣的研究，也使读者能够自己实现和实验遗传算法。它深入关注了一小部分重要而有趣的主题——特别是在机器学习、科学建模和人工生命方面——并回顾了广泛的研究，包括米切尔和她的同事们的工作。对应用程序和建模项目的描述超越了计算机科学的严格界限，包括动力系统理论、博弈论、分子生物学、生态学、进化生物学和群体遗传学，强调了遗传算法作为可跨学科使用的搜索方法的令人兴奋的“通用”性质。遗传算法导论适用于任何科学学科的学生和研究人员。它包括许多思想和计算机练习，建立和加强读者对文本的理解。第一章介绍了遗传算法及其术语，并详细描述了两个具有争议的应用。第二章和第三章探讨了遗传算法在机器学习（计算机程序、数据分析和预测、神经网络）和科学模型（学习、进化和文化之间的相互作用；性选择；生态系统；进化活动）中的应用。第四章深入讨论了遗传算法理论的几种方法。第五章讨论了实现，最后一章提出了一些目前尚未解决的问题，并展望了进化计算的未来。
From the Publisher: "This is the best general book on Genetic Algorithms written to date. It covers background, history, and motivation; it selects important, informative examples of applications and discusses the use of Genetic Algorithms in scientific models; and it gives a good account of the status of the theory of Genetic Algorithms. Best of all the book presents its material in clear, straightforward, felicitous prose, accessible to anyone with a college-level scientific background. If you want a broad, solid understanding of Genetic Algorithms -- where they came from, what's being done with them, and where they are going -- this is the book. -- John H. Holland, Professor, Computer Science and Engineering, and Professor of Psychology, The University of Michigan; External Professor, the Santa Fe Institute. 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, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines. An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.