Wirsansky Hands-On Genetic Algorithms with Python

Apply genetic algorithms to solve real-world AI and machine learning problems
2. Auflage 2024
ISBN: 978-1-80512-157-2
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 418 Seiten

Written by Eyal Wirsansky, a senior data scientist and AI researcher with over 25 years of experience and a research background in genetic algorithms and neural networks, Hands-On Genetic Algorithms with Python offers expert insights and practical knowledge to master genetic algorithms.
After an introduction to genetic algorithms and their principles of operation, you'll find out how they differ from traditional algorithms and the types of problems they can solve, followed by applying them to search and optimization tasks such as planning, scheduling, gaming, and analytics. As you progress, you'll delve into explainable AI and apply genetic algorithms to AI to improve machine learning and deep learning models, as well as tackle reinforcement learning and NLP tasks. This updated second edition further expands on applying genetic algorithms to NLP and XAI and speeding up genetic algorithms with concurrency and cloud computing. You'll also get to grips with the NEAT algorithm. The book concludes with an image reconstruction project and other related technologies for future applications.
By the end of this book, you'll have gained hands-on experience in applying genetic algorithms across a variety of fields, with emphasis on artificial intelligence with Python.

Autoren/Hrsg.

Wirsansky, Eyal

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Spracherkennung, Sprachverarbeitung

Inhaltsverzeichnis

Table of Contents - An Introduction to Genetic Algorithms
- Understanding the Key Components of Genetic Algorithms
- Using the DEAP Framework
- Combinatorial Optimization
- Constraint Satisfaction
- Optimizing Continuous Functions
- Enhancing Machine Learning Models Using Feature Selection
- Hyperparameter Tuning Machine Learning Models
- Architecture Optimization of Deep Learning Networks
- Reinforcement Learning with Genetic Algorithms
- Natural Language Processing
- Explainable AI and Counterfactuals
- Speeding Up Genetic Algorithms with Concurrency
- Harnessing the Cloud
- Genetic Image Reconstruction
- Other Evolutionary and Bio-Inspired Computation Techniques

Über Autor(innen)

Wirsansky Eyal :

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.