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Artificial Intelligence with Python

Artificial Intelligence with Python

By : Prateek Joshi
3.6 (34)
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Artificial Intelligence with Python

Artificial Intelligence with Python

3.6 (34)
By: Prateek Joshi

Overview of this book

Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more. Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.
Table of Contents (17 chapters)
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Solving the symbol regression problem


Let's see how to use genetic programming to solve the symbol regression problem. It is important to understand that genetic programming is not the same as genetic algorithms. Genetic programming is a type of evolutionary algorithm in which the solutions occur in the form of computer programs. Basically, the individuals in each generation would be computer programs and their fitness level corresponds to their ability to solve problems. These programs are modified, at each iteration, using a genetic algorithm. In essence, genetic programming is the application of a genetic algorithm.

Coming to the symbol regression problem, we have a polynomial expression that needs to be approximated here. It's a classic regression problem where we try to estimate the underlying function. In this example, we will use the expression: f(x) = 2x^3 - 3x^2 + 4x - 1

The code discussed here is a variant of the symbol regression problem given in the DEAP library. Create a new...

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Artificial Intelligence with Python
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