Evolving Neural Networks for Adaptive Autonomous Agents
Abstract
Evolving neural networks have gained significant attention in the field of artificial intelligence, particularly for developing adaptive autonomous agents. In this paper, we explore the application of evolutionary algorithms to train neural networks for creating intelligent agents capable of adapting to complex and dynamic environments. We present a comprehensive study of various evolutionary strategies, including genetic algorithms and neuroevolution, to evolve neural network architectures and weights. These agents exhibit remarkable adaptability in tasks such as reinforcement learning, robotics, and game playing. We discuss the advantages and challenges associated with evolving neural networks and their potential to revolutionize autonomous systems.
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References
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