  Evolutionary optimization is a generic population-based metaheuristic that
can be adapted to solve a wide variety of optimization problems and has proven
very effective for combinatorial optimization problems. However, the potential
of this metaheuristic has not been utilized in Distributed Constraint
Optimization Problems (DCOPs), a well-known class of combinatorial optimization
problems prevalent in Multi-Agent Systems. In this paper, we present a novel
population-based algorithm, Anytime Evolutionary DCOP (AED), that uses
evolutionary optimization to solve DCOPs. In AED, the agents cooperatively
construct an initial set of random solutions and gradually improve them through
a new mechanism that considers an optimistic approximation of local benefits.
Moreover, we present a new anytime update mechanism for AED that identifies the
best among a distributed set of candidate solutions and notifies all the agents
when a new best is found. In our theoretical analysis, we prove that AED is
anytime. Finally, we present empirical results indicating AED outperforms the
state-of-the-art DCOP algorithms in terms of solution quality.
