Book Image

Artificial Intelligence with Python - Second Edition

By : Alberto Artasanchez, Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: Alberto Artasanchez, Prateek Joshi

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
24
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25
Index

Genetic programming use cases

As discussed in one of the early chapters, Genetic Algorithms (GA) and Genetic Programming (GP) are one of the "five tribes" of machine learning.

Figure 14: The five tribes (Pedro Domingos)

Since its early beginnings, GP has produced a wide variety of advances. The literature, which covers thousands of applications of GP contains many use cases where GP has been applied successfully. Exhaustively covering that list would be beyond the scope of the book, but we list a few of the more important ones here.

Here, we begin a discussion of the general kinds of problems where GP has been applied successfully, and then review a representative subset of each of the main application areas of GP. Areas where GP has done well, based on the experience of a diverse and wide group of researchers over the years, include:

Poorly understood domains

This is where the interrelationships among the relevant variables is unknown or poorly...