Neuroevolution is a form of artificial intelligence (AI) that leverages evolutionary algorithms to generate artificial neural networks (ANNs), parameters, architectures, or learning rules. It’s a subfield of machine learning (ML) that combines neural networks and evolutionary computation.


Neuroevolution is a technique that applies evolutionary algorithms to train ANNs. It’s particularly useful in reinforcement learning and unsupervised learning scenarios where the optimal solution is not known in advance. Neuroevolution can optimize the weights, architectures, and learning rules of ANNs, making it a versatile tool in the AI toolbox.

How Neuroevolution Works

Neuroevolution starts with a population of neural networks with random weights. These networks are evaluated based on a fitness function, which measures how well each network solves the problem at hand. The best-performing networks are selected and used to create a new generation of networks through mutation (random changes) and crossover (combining parts of two networks). This process is repeated for many generations until a satisfactory solution is found.

Applications of Neuroevolution

Neuroevolution has been successfully applied in various fields, including robotics, game playing, function approximation, and control problems. It’s particularly effective in environments with high-dimensional inputs and outputs, where traditional reinforcement learning methods struggle.

Advantages of Neuroevolution

  1. Adaptability: Neuroevolution can adapt to changing environments, making it suitable for real-world applications where conditions can change over time.
  2. Scalability: It can handle high-dimensional inputs and outputs, making it suitable for complex tasks.
  3. Parallelizability: The evaluation of each individual in the population can be done in parallel, making neuroevolution highly scalable with more computational resources.

Limitations of Neuroevolution

  1. Computationally Intensive: Neuroevolution can be computationally expensive, especially for large networks and populations.
  2. Local Optima: Like other evolutionary algorithms, neuroevolution can get stuck in local optima.

Key Takeaways

Neuroevolution is a powerful technique that combines the strengths of neural networks and evolutionary algorithms. It’s particularly useful in reinforcement learning and unsupervised learning scenarios, and has been successfully applied in various fields. Despite its computational intensity and the risk of local optima, its adaptability, scalability, and parallelizability make it a valuable tool in the AI and ML fields.

Further Reading

  1. NeuroEvolution of Augmenting Topologies (NEAT)
  2. Evolving Neural Networks through Augmenting Topologies
  3. A Neuroevolution Approach to General Atari Game Playing
  • Artificial Neural Networks (ANNs)
  • Evolutionary Algorithms
  • Reinforcement Learning
  • Unsupervised Learning
  • Machine Learning (ML)
  • Artificial Intelligence (AI)