Melanie Mitchell has written an outstanding—and needed—new text for the burgeoning field for genetic algorithms. The book combines a clear explanation of GA basics and implementation, penetrating discussions of 15 of the most significant recent GA researches in problem solving and scientific modelling, and the first major review of GA theory from Holland's original concepts to recent advances and controversies.
There are over references. Mitchell's work is sure to become the field's new standard source and text. 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. Melanie Mitchell has written an excellent introduction to genetic algorithms, one of the most promising branches of machine learning.
GAs, with their minimal demands on the programmer together with heavy exploitation of computing cycles, and uniquely well-positioned to take advantage of the vastly increased availability of computer cycles made covering both traditional GA methods and the recent wealth of GA variants, and also providing details on GA implementation, theoretical foundations, and scientific applications. Melanie Mitchel has successfully assembled a collection of recent applications that convey the excitement and potential of genetic algorithms in solving an array of otherwise difficult or intractable problems.
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. 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. Melanie Mitchell. Luis M. Jordan Pollack , Mark A.
Bedau , Phil Husbands , Richard A. Watson , and Takashi Ikegami. Search Search. Search Advanced Search close Close. Add to Cart Buying Options. Request Permissions Exam copy. Overview Author s Praise. Summary Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. Instructor Resources Downloadable instructor resources available for this title: solution manual.
Share Share Share email. Each chapter is a step-by-step tutorial that helps to build your skills at using genetic algorithms to solve problems using Python. Python is a high-level, low ceremony and powerful language whose code can be easily understood even by entry-level programmers.
If you have experience with another programming language then you should have no difficulty learning Python by induction. This site comply with DMCA digital copyright. We do not store files not owned by us, or without the permission of the owner. We also do not have links that lead to sites DMCA copyright infringement. If You feel that this book is belong to you and you want to unpublish it, Please Contact us. Genetic Algorithms with Python. Download e-Book.
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