Evolutionary optimization of emergent phenomena in multi-agent systems using heuristic approach for fitness evaluation

In order to design a multi-agent system with required emergent phenomena, evolutionary optimization can be used. The downside of this approach is large time needed to perform optimization due to the simulation of the multi-agent system that has to be carried out every time fitness function is evaluated. In the case when a single simulation result is not reliable, more than one simulation has to be executed for a fitness value evaluation, which is even more time-consuming. The research presented in this paper investigates improvements of evolutionary optimization of multi-agent systems when multiple simulations of the system are needed for fitness function evaluation. The improvement is based on a heuristic method for multi-agent system fitness evaluation. The proposed method considerably enhances fitness evaluation reliability by taking into account simulations completed in previous generations. For that reason the multiple simulations fitness value is constructed gradually over many generations, whereas a heuristic function is used for leveling fitness values based on a different number of multi-agent system simulations. The experimental results show that proposed method improves results of evolutionary optimization of emergent phenomena in multi-agent systems compared to the standard method, where a fitness function is evaluated based on a single system simulation, while using virtually the same execution time for the optimization process.