10 Resources To Learn Genetic Programming

To help professionals starting out or looking to gain additional knowledge, we have listed 10 sources including, books, ebooks, videos and tutorials.

Genetic programming is one of the most interesting aspects of machine learning and AI, where computer programs are encoded as a set of genes that are then modified (evolved) using an evolutionary algorithm. It is picking up as one of the most sought after research domains in AI where data scientists use genetic algorithms to evaluate genetic constituency. While research is still underway in this area, many researchers and professionals are now looking to dig into the subject. To help those professionals starting out in the field and for those looking to gain additional knowledge, we have listed 10 sources including, books, ebooks, videos and tutorials that will help to know more about genetic programming.

The resources are listed in no particular order.

1| Introduction to Genetic Algorithms by Melanie Mitchell (Book):

It is one of the most read books on genetic algorithms and covers in-depth details about the subject such as background, history, motivation along with informative examples that makes it easy to understand the concepts. It also discusses use cases of genetic algorithms in scientific models, which is a good read for anyone wanting to know more about the area. It also gives an insight into some of the most interesting research in the field enabling readers to experiment and implement genetic algorithms of their own.

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The book can be bought here.

2| Genetic Algorithms in search, optimisation and machine learning by David E Goldberg (Book):

Authored by David E. Goldberg, the book is a comprehensive text for students pursuing Computer Science Engineering, Electrical Engineering and Electronics Engineering. The book is also useful for practitioners who are looking to learn more about the field. It has procedures and applications explained in detail where the author has brought together computer techniques, mathematical tools and research results giving a complete insight into the subject.

The free ebook can be downloaded here.

The book can be bought here.

3| Colorado State University tutorial on Genetic Algorithms by Darell Whitley (Online tutorial):

This tutorial covers the canonical genetic algorithm along with experimental forms of the genetic algorithm, including the parallel island model and parallel cellular genetic algorithm. It illustrates a genetic search with hyperplane sampling. It is explained by Darrell Whitley from the computer science department of Colorado State University and is explained in detail with examples, illustrations and use cases.

The PDF version of the tutorial can be downloaded here.

4| A Field Guide to Genetic Programming by Riccardo Poli Poli,  William B. Langdon, Nicholas Freitag McPhee (Book):

One of the most hands-on guides on the subject, the book has received good reviews from the data science community. The book begins by explaining the basics of genetic programming. The subject has been explained with stress on use cases as genetic programming has generated a plethora of human-competitive results including novel scientific discoveries and patentable inventions. The three researchers have brought a unique perspective of this technique on the bok.

The free ebook can be downloaded here.

The book can be bought here.

5| Introduction to Genetic Algorithms: Theory and Applications by Udemy (Video):

In this video tutorial by Udemy, you can learn the main mechanisms of the genetic algorithm as a heuristic artificial intelligence search or optimisation in Matlab. It covers tutorials on using a genetic algorithm to solve optimisation problems, analysing the performance, modifying or improving genetic algorithms and more. It covers the most fundamental aspects of the subject and is one of the best sources if you are new to the field.

The video can be watched here.

6| MIT Lecture on Learning Genetic Algorithm by Patrick H. Winston (Video):

Conducted by Patrick H. Winston, an American computer scientist, and professor at the Massachusetts Institute of Technology. This lecture explores the genetic algorithm at a conceptual level. The instructor has tried to consider three approaches on how a population evolves towards desirable traits, ending with ranks of both fitness and diversity. He has discussed it with use cases and live examples.

The video can be downloaded here.

7| Clever Algorithms: Nature-Inspired Programming Recipes by Jason Brownlee (Book):

It covers evolutionary algorithms in detail which is concerned with computational methods inspired by the process and mechanisms of biological evolution. It covers extensively the genetic algorithm, genetic programming, evolution strategies, evolutionary programming, differential evolution and more.

The book can be downloaded here.

8| The Algorithm Design Manual by Steve Skiena (Book):

This book covers an extensive section on genetic algorithms and other interesting heuristics for solving various types of problems. It deals with some key algorithms while drawing the author’s own real-world experiences on design and analysis. The first half of the book is a general guide to techniques for the design and analysis of computer algorithms while the second part includes a catalogue of the 75 most important algorithmic problems.

The book can be bought here.

9| Collective Intelligence by OReilly by Toby Segaran (Book):

Programming Collective Intelligence takes you into the world of machine learning and statistics and explains how to draw conclusions about user experience, marketing, personal tastes, and human behaviour in general,  all from information that you and others collect every day. This book has a chapter on the genetic algorithm that has been covered with illustrating examples.

The book can be bought here.

10| Practical Genetic Algorithms by Randy L. Haupt and Sue Ellen Haupt (Book):

This book stresses genetic algorithms with an emphasis on practical applications. It provides numerous practical example problems and contains over 80 illustrations including figures, tables, a list of genetic algorithm routines in pseudocode, and more.

The book can be bought here.

Srishti Deoras
Srishti currently works as Associate Editor at Analytics India Magazine. When not covering the analytics news, editing and writing articles, she could be found reading or capturing thoughts into pictures.

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