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.
1. Introduction to Genetic Algorithms by Melanie Mitchell
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 algorithm 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 with genetic algorithms of their own.
2. Genetic Algorithms in search, optimization and machine learning by David E Goldberg
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 detailed where the author has brought together computer techniques, mathematical tools and research results giving a complete insight into the subject.
3. Colorado State University tutorial on Genetic Algorithms by Darell Whitley
This tutorial covers the canonical genetic algorithm along with experimental forms of the genetic algorithm, including 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.
4. A Field Guide to Genetic Programming by Riccardo Poli Poli, William B. Langdon, Nicholas Freitag McPhee
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.
5. Introduction to Genetic Algorithms: Theory and Applications by Udemy
In this video tutorial by Udemy, you can learn the main mechanisms of the genetic algorithm as a heuristic artificial intelligence search or optimization in Matlab. It covers tutorial on using a genetic algorithm to solve optimization problems, analyzing the performance, modifying or improving genetic algorithm 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.
6. MIT Lecture on Learning Genetic Algorithm by Patrick H. Winston
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.
7. Clever Algorithms: Nature-Inspired Programming Recipes by Jason Brownlee
It covers evolutionary algorithms in detail which is concerned with computational methods inspired by the process and mechanisms of biological evolution. It covers extensively about the genetic algorithm, genetic programming, evolution strategies, evolutionary programming, differential evolution and more.
8. The Algorithm Design Manual by Steve Skiena
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 authorÔ??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 catalog of the 75 most important algorithmic problems.
9. Collective Intelligence by OReilly by Toby Segaran
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 behavior 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.
10. Practical Genetic Algorithms by Randy L. Haupt and Sue Ellen Haupt
This book stresses on 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.