Explanation
Imagine you're trying to design the perfect paper airplane. You build a bunch of slightly different planes, test them out, and keep the ones that fly the furthest. Then, you make copies of those good planes, introduce slight variations, and test again. You repeat this process over and over, each time getting closer to the ultimate design.
Neuroevolution works in a similar way, but instead of paper airplanes, it's designing artificial neural networks. It's a type of machine learning where algorithms mimic the process of natural selection to evolve the structure and parameters of neural networks.
Think of it as a genetic algorithm for AI. The algorithm starts with a population of random neural networks, evaluates their performance on a specific task, and selects the best-performing ones to reproduce. During reproduction, the algorithm introduces small mutations, creating new variations of the networks. This process is repeated over many generations, gradually improving the performance of the networks.
It's particularly useful when the optimal architecture of a neural network isn't known in advance, or when the problem is complex and difficult to solve with traditional training methods. The neural networks evolve over time, finding solutions and automations that may not be achievable via standard programming.
Examples
Consumer Example
Consider a video game where the AI opponents are constantly getting smarter and more challenging to play against.
Neuroevolution can be used to train the AI in these games, allowing them to learn new strategies and adapt to the player's behaviour in real-time. The AI evolves with the player, ensuring a continuously engaging and challenging gameplay experience.
Business Example
Imagine a company trying to optimise its supply chain. Neuroevolution can be used to design a neural network that predicts demand, optimises inventory levels, and routes shipments more efficiently.
The system can learn from historical data, adapt to changing market conditions, and improve its performance over time, leading to significant cost savings and improved customer satisfaction. In this example, AI can enable the supply chain to be much more flexible, responsive and resilient.