  An original approach, termed Divide-and-Evolve is proposed to hybridize
Evolutionary Algorithms (EAs) with Operational Research (OR) methods in the
domain of Temporal Planning Problems (TPPs). Whereas standard Memetic
Algorithms use local search methods to improve the evolutionary solutions, and
thus fail when the local method stops working on the complete problem, the
Divide-and-Evolve approach splits the problem at hand into several, hopefully
easier, sub-problems, and can thus solve globally problems that are intractable
when directly fed into deterministic OR algorithms. But the most prominent
advantage of the Divide-and-Evolve approach is that it immediately opens up an
avenue for multi-objective optimization, even though the OR method that is used
is single-objective. Proof of concept approach on the standard
(single-objective) Zeno transportation benchmark is given, and a small original
multi-objective benchmark is proposed in the same Zeno framework to assess the
multi-objective capabilities of the proposed methodology, a breakthrough in
Temporal Planning.
