Evolutionary synthesis of multi-agent systems for dynamic dial-a-ride problems

In dynamic dial-a-ride problems a fleet of vehicles need to handle transportation requests within time. We research how to create a decentralized multi-agent system that can solve the dynamic dial-a-ride problem. Normally multi-agent systems are hand designed for each specific application. In this paper we research the applicability of genetic programming to automatically program a multi-agent system that solves dial-a-ride problems. We evaluated the evolved system by running a number of simulations and compared it's performance to a selection hyper-heuristic. The results shows that genetic programming can be a viable alternative to hand constructing multi-agent systems.

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