Book Image

Artificial Intelligence with Python - Second Edition

By : Prateek Joshi
Book Image

Artificial Intelligence with Python - Second Edition

By: 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

Fundamental concepts in genetic algorithms

In order to build a GA, we need to understand several key concepts and terms. These concepts are used extensively throughout the field of GAs to build solutions to various problems. One of the most important aspects of GAs is randomness. In order to iterate, it relies on the random sampling of individuals. This means that the process is non-deterministic. So, if you run the same algorithm multiple times, you might end up with different solutions.

Let's now define the term population. A population is a set of individuals that are possible candidate solutions. In a GA, the single best solution is not maintained at any given stage but rather a set of potential solutions, one of which could be the best. But the other solutions play an important role during the search. Since the population of solutions is tracked, it is less likely to get stuck in a local optimum. Getting stuck in a local optimum is a classic problem faced by other optimization...