Free PDF Genetic Algorithms with Python, by Clinton Sheppard
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Genetic Algorithms with Python, by Clinton Sheppard
Free PDF Genetic Algorithms with Python, by Clinton Sheppard
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About the Author
I am a polyglot programmer with more than 15 years of professional programming experience. When learning a new programming language, I start with a familiar problem and try to learn enough of the new language to solve it. For me, an engine for solving genetic algorithms is that familiar problem. Why? For one thing, it is a project where I can explore interesting puzzles, and where even a child's game like Tic-tac-toe can be viewed on a whole new level. Also, I can select increasingly complex puzzles to drive evolution in the capabilities of the engine. This allows me to discover the expressiveness of the language, the power of its tool chain, and the size of its development community as I work through the idiosyncrasies of the language.
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Product details
Hardcover: 312 pages
Publisher: Clinton Sheppard (February 11, 2018)
Language: English
ISBN-10: 1732029806
ISBN-13: 978-1732029804
Product Dimensions:
7.5 x 0.8 x 9.2 inches
Shipping Weight: 2.4 pounds (View shipping rates and policies)
Average Customer Review:
4.1 out of 5 stars
8 customer reviews
Amazon Best Sellers Rank:
#2,805,112 in Books (See Top 100 in Books)
I hate to be the Debbie Downer here, but...this book has you trudging through hours of idiosyncratic python code just to get to a program that flips bits from 0 to 1. It's another python book that succeeds in taking the fun and exploration out of programming. The author claims that the language used is not that important, but the obtuse nature of the code belies a possible primary intent to turn a personal code library into an additional source of income, with the goal of teaching the material in an engaging way taking a secondary back-seat.I remember many years ago, in a computer science class, using Lisp and genetic algorithms to optimize the travelling salesman problem. I was pretty amazed at the results that were obtained with maybe 3/4 page worth of code. That is how you get someone engaged in a powerful programming paradigm. This is not the way. There may be a lot of good, even very good information, in this book. But, the amount of boilerplate and noise is just unreasonable.
I bought GAWP over a year ago, when I was working on a Genetic Algorithm chapter for my book Math Adventures with Python. I've had a lot of experience with Python, so I didn't need a tutorial on strings and variables. If you're new to Python or programming, you might want to start with another book. Sheppard throws the reader into the deep end. But the projects cover all the classics of GA's, like the 8 Queens Puzzle, Magic Squares, Sudoku and the project I was particularly interested in, the Traveling Salesman Problem.Other reviewers have complained about the author's use of Python's unittest module, but it might be the reason his code runs 2 or 3 times faster than mine. It's a great hands-on introduction to testing, as well. Another bonus is Sheppard recommends using Pycharm for an editor.After writing a bunch of code to solve the first problem in the book, Guess the Password, Sheppard takes the code that will just be repeated in every future program and extracts it to a "genetic" file that will be reused, possibly with some modifications, for the rest of the book. This risky move is a stroke of genius, but one that is clearly lost on some reviewers. It's true, the author only includes a handful of graphics and charts in the entire book, and it's not easy to keep track of which function/method goes where at first (or second!).Having said all that, the book contains absolutely indispensable projects for the intermediate programmer interested in really delving into using Genetic Algorithms to solve puzzles and problems. Challenging as it is, I keep going back to this book to work on problems. It's not the type of book you'll only go through once; but you'll keep learning something every time you work on one of its projects. All the code is available in the appendix and online.
Overall, good introduction to genetic algorithms for those without a mathematical background. Focuses on a high level view of concepts and applications.The Good: The author is clear, which makes the book easy to read. The code is written well, albeit, for the consumption of a CS major than a generalist. The examples are short and fun to work with. The book is much easier to read than more rigorous texts on genetic algorithms.The Bad: The author chose to use `unittest` for running the genetic algorithms which makes the code difficult to read at times, and kind of annoying to work with. I'm a statistician, and although I do run tests for my programs, I prefer to not use `unittest` unless it's absolutely necessary.Overall, I like this book, even though the print quality may not be great. But the low print quality may be the reason why this book is affordable.
I like this book a lot. One of the things I like about it is that the author takes a lot of different examples, and step-by-step teaches you the elements of genetic algorithms, and also improves the algorithms over the course of the book.I'd say if you want to learn genetic algorithms this is certainly a good book. I'll be looking for some more theoretical books to round out my knowledge, but this is a great start for someone who knows python, but isn't super familiar with how genetic algorithms work.One of the things I don't like about the book is that it doesn't actually talk about the overall concept of what genetic algorithms do in a way that makes it super easy to apply to other problems. I do think the point of the step-by-step approach was to give you some of that, but somehow it didn't quite work for me I needed a little bigger picture explanations of the conceptual frameworks behind the code that he uses.But the author does, with each subsequent chapter, add new, more complex concepts and new ways to do mutation and checking fitness, which is great.
I got the first program working and am working through the rest of the material. It focuses heavily on the Python programming environment. I have read and understand the first half of the first chapter. The author jumps quickly into object oriented programming style which I am not familiar. i am hoping to extract information about how to design genotypes and fitness tests. I am trying to use the information to program an artificial neural network. I will continue to think and study this book. It seems to be a book worth the time to learn. My background in procedural programming style of programming is being challenged by the conversion to object oriented programming style.
An excellent introduction to genetic algorithms. If you are someone who is interested in NP type problems and their solutions, this book is definitely for you!
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