Expert-level Explanation: Neuroevolution leverages the principles of natural selection to evolve neural network architectures and weights, optimizing them for specific tasks without the need for gradient-based training methods. This process involves generating a population of neural networks, evaluating their performance on a given task, selecting the best-performing networks, and using genetic operators such as crossover and mutation to produce a new generation of networks. Neuroevolution is particularly useful in scenarios where traditional gradient-based methods struggle, such as in reinforcement learning, where the reward signal is sparse or delayed. It also excels in creating novel architectures, potentially leading to more efficient and effective network designs that might not be discovered through standard techniques.
Historical Overview: The concept of neuroevolution emerged in the late 1980s, with early work gaining attention in the 1990s as computational power increased. It gained significant popularity in the 2000s with the development of more sophisticated evolutionary algorithms and increased interest in complex, adaptive systems.
Key Contributors: Notable contributors to the field of neuroevolution include Kenneth Stanley, known for his work on the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, which introduced the idea of evolving both the topology and weights of neural networks. Other significant figures include Peter J. Angeline and David B. Fogel, who made early contributions to the application of evolutionary computation to neural network training.