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Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python

By : Omelianenko
3 (1)
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Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python

3 (1)
By: 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)
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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

Tuning hyperparameters

With proper tuning of the hyperparameters, you can make tremendous improvements in the training speed and efficiency of the neuroevolution process. Here are some practical tips:

  • Do short runs with different seed values of the random number generator and note how the algorithm performance changes. After that, choose the seed value that gives the best performance and use it for the long runs.
  • You can increase the number of species in the population by decreasing the compatibility threshold and by slightly increasing the value of the disjoint/excess weight coefficient.
  • If the process of neuroevolution has stumbled while trying to find a solution, try to decrease the value of the NEAT survival threshold. This coefficient maintains the ratio of the best organisms within a population that got the chance to reproduce. By doing this, you increase the quality of...

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