In this chapter, we learned about genetic algorithms and their underlying concepts. We discussed evolutionary algorithms and genetic programming. We understood how they are related to genetic algorithms. We discussed the fundamental building blocks of genetic algorithms including the concepts of population, crossover, mutation, selection, and fitness function. We learned how to generate a bit pattern with predefined parameters. We discussed how to visualize the evolution process using CMA-ES. We learnt how to solve the symbol regression problem in this paradigm. We then used these concepts to build a robot controller to traverse a map and consume all the targets. In the next chapter, we will learn about reinforcement learning and see how to build a smart agent.
Artificial Intelligence with Python
Artificial Intelligence with Python
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 (23 chapters)
Artificial Intelligence with Python
Credits
About the Author
About the Reviewer
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Preface
Free Chapter
Introduction to Artificial Intelligence
Classification and Regression Using Supervised Learning
Predictive Analytics with Ensemble Learning
Detecting Patterns with Unsupervised Learning
Building Recommender Systems
Logic Programming
Heuristic Search Techniques
Genetic Algorithms
Building Games With Artificial Intelligence
Natural Language Processing
Probabilistic Reasoning for Sequential Data
Building A Speech Recognizer
Object Detection and Tracking
Artificial Neural Networks
Reinforcement Learning
Deep Learning with Convolutional Neural Networks
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