Divide-and-Evolve: A New Memetic Scheme for Domain-Independent Temporal Planning

@incollection{citeulike:2894421, abstract = {An original approach, termed 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 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 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.}, author = {Schoenauer, Marc and Sav\'{e}ant, Pierre and Vidal, Vincent }, citeulike-article-id = {2894421}, doi = {http://dx.doi.org/10.1007/11730095\_21}, journal = {Evolutionary Computation in Combinatorial Optimization}, keywords = {constraint-programming, evolutionary-computation, hybridization, metaheuristic, planning, temporal-planning}, pages = {247--260}, posted-at = {2008-06-14 09:56:07}, priority = {2}, title = {Divide-and-Evolve: A New Memetic Scheme for Domain-Independent Temporal Planning}, url = {http://dx.doi.org/10.1007/11730095\_21}, year = {2006} }

TY - CHAP ID - citeulike:2894421 L3 - citeulike-article-id:2894421 N2 - An original approach, termed 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 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 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. JF - Evolutionary Computation in Combinatorial Optimization EP - 260 TI - Divide-and-Evolve: A New Memetic Scheme for Domain-Independent Temporal Planning SP - 247 KW - constraint-programming KW - evolutionary-computation KW - hybridization KW - metaheuristic KW - planning KW - temporal-planning AU - Schoenauer, Marc AU - Savéant, Pierre AU - Vidal, Vincent PY - 2006/// UR - http://dx.doi.org/10.1007/11730095_21 ER -