An Evolutionary Algorithm for Autonomous Agents with Spiking Neural Networks

Inspired by the evolution of biological brains, the study of neurally-driven evolved autonomous agents has received more and more attention. In this paper, we propose an evolutionary algorithm for neurally-driven autonomous agents, each agent is controlled by a spiking neural network, and the network receives the sensory inputs and processes the motor outputs through the encoded spike information. The controlling spiking neural networks of autonomous agents are developed by the evolutionary algorithms that apply some of genetic operators and selection to a population of agents that undergo evolution. The corresponding food gathering experiment results show that the autonomous agents appear intelligent behaviours for the simulation environment. Additionally, the parameters of networks and agents play an important role in the evolutionary process.

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