You're reading from Hands-On Genetic Algorithms with Python

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838557744

Edition1st Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Artificial Intelligence

Author (1)

Eyal Wirsansky

Limitations of genetic algorithms

To get the most out of genetic algorithms, we need to be aware of their limitations and potential pitfalls.

The limitations of genetic algorithms are as follows:

The need for special definitions
The need for hyperparameter tuning
Computationally-intensive operations
The risk of premature convergence
No guaranteed solution

We will cover each of these in the upcoming sections.

Special definitions

When applying genetic algorithms to a given problem, we need to create a suitable representation for them – define the fitness function and the chromosome structure, as well as the selection, crossover, and mutation operators that will work for this problem. This can often prove to be challenging...

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Author (1)

Eyal Wirsansky

Eyal Wirsansky is a senior data scientist, an experienced software engineer, a technology community leader, and an artificial intelligence researcher. Eyal began his software engineering career over twenty-five years ago as a pioneer in the field of Voice over IP. He currently works as a member of the data platform team at Gradle, Inc. During his graduate studies, he focused his research on genetic algorithms and neural networks. A notable result of this research is a novel supervised machine learning algorithm that integrates both approaches. In addition to his professional roles, Eyal serves as an adjunct professor at Jacksonville University, where he teaches a class on artificial intelligence. He also leads both the Jacksonville, Florida Java User Group and the Artificial Intelligence for Enterprise virtual user group, and authors the developer-focused artificial intelligence blog, ai4java.
Read more about Eyal Wirsansky

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