Book Image

Hands-On Neuroevolution with Python

By : Iaroslav Omelianenko
Book Image

Hands-On Neuroevolution with Python

By: Iaroslav Omelianenko

Overview of this book

Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods
4
Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems
9
Section 3: Advanced Neuroevolution Methods
14
Section 4: Discussion and Concluding Remarks

Exercises

  1. Try to increase the compatibility_disjoint_coefficient parameter in the maze_config.ini file and run the experiment with new settings. What impact does this modification have on the number of species produced during the evolution? Is the neuroevolution process able to find a successful maze solver?
  2. Increase the population size by 200% (the pop_size parameter). Was the neuroevolution process able to find a solution in this case, and if so, how many generations did it take?
  3. Change the seed value of the random number generator (see line 118 of the maze_experiment.py file). Does the neuroevolution process succeed with this new value?