Genetic Algorithms with Python Paperback – April 29, 2016

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.

This was a great book to learn Python as well as genetic algorithms. I thought the problems were very interesting and the code is written so that it gets more sophisticated with each problem. A highly recommend this book if you are looking for some hands-on time with Python and genetic algorithms. There's a lot of problems to solve, making it a great value.

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.

This title is very code intensiveThe chapter ' Generating Sudoku 'Is very valuable . Many other titles are for the solving of Sudoku which is all well and good but by generating aSudoku far more mathematics is learned.Clinton Sheppard has done agreat job combining math and coding.

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!

Cryptic Python. Difficult to read and follow. Code only, no theory. I regret buying it.

Excellent Value

I would give the book an A- for the approach to coding in Python. I have learned a bit of Python from it including unittest. However, it would have gotten an A on coding if the code was not written as a pure unit test.I give it a D for it's coverage of genetic algorithms. The Simulated Annealing is just an implementation of random selection. That is NOT Simulated Annealing. Nowhere in the algorithm implementation is there any mention of how to control "temperature" or how to use it in your algorithm development. The discussion of mutation and crossover are simply random choices. No attention paid to which neighbors are nearest; just generate using the random function a new gene and see if it is better than the last.Sheppard does give a number of suggestions to improve the algorithm, but no details.Did I get my money's worth? Yes I did. Would do it again. However, I was looking for more insight in the application of Genetic Algorithms and that was not there.