evaluating the evolvability of emergent agents with different numbers of states
Emergence is an important and promising scientific topic today because it offers benefits that can not be achieved by classic means. But it is often challenging to control emergence and to find correct local rules that create desired global behavior. It especially becomes difficult if the search space representing the problem that has to be optimized is not continuous/linear. One solution to that problem is evolution. This paper shows that the use of Genetic Algorithms is feasible for such problems by the example of the Creatures' Exploration Problem in which agents shall visit all non-blocked cells in a grid. Different amounts of agents and states per agent are evolved and statistically compared. It shows that neither a single extension of agent capabilities nor sole increase of agent numbers provides the best performance. The results hint that a mixture of both should be used instead.
optimization genetic algorithms multi-agent systems cellular automata fitness landscape