Abstract
Divide-and-Evolve (DaE) is an original “memeticization” of Evolutionary Computation and Artificial Intelligence Planning. DaE optimizes either the number of actions, or the total cost of actions, or the total makespan, by generating ordered sequences of intermediate goals via artificial evolution, and calling an external planner to solve each subproblem in turn. DaE can theoretically use any embedded planner. However, since the introduction of this approach only one embedded planner had been used: the temporal optimal planner CPT. In this paper, we propose a new version of DaE, using time-based Atom Choice and embarking the sub-optimal planner YAHSP in order to test the robustness of the approach and to evaluate the impact of using a sub-optimal planner rather than an optimal one, depending on the type of planning problem.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bibai, J., Schoenauer, M., Savéant, P.: Divide-And-Evolve Facing State-of-the-Art Temporal Planners during the 6th International Planning Competition. In: Cotta, C., Cowling, P. (eds.) EvoCOP 2009. LNCS, vol. 5482, pp. 133–144. Springer, Heidelberg (2009)
Bibai, J., Savéant, P., Schoenauer, M., Vidal, V.: DAE: Planning as Artificial Evolution (Deterministic part). In: At International Planning Competition, IPC (2008), http://ipc.icaps-conference.org/
Bibai, J., Savéant, P., Schoenauer, M., Vidal, V.: Learning Divide-and-Evolve Parameter Configurations with Racing. In: Coles, A., et al. (eds.) ICAPS 2009, Workshop on Planning and Learning. AAAI Press, Menlo Park (2009)
Brié, A.H., Morignot, P.: Genetic Planning Using Variable Length Chromosomes. In: Proc. ICAPS (2005)
Fikes, R., Nilsson, N.: STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving. Artificial Intelligence 1, 27–120 (1971)
Fox, M., Long, D.: PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains. JAIR 20, 61–124 (2003)
Gerevini, A., Saetti, A., Serina, I.: On Managing Temporal Information for Handling Durative Actions in LPG. In: Cappelli, A., Turini, F. (eds.) AI*IA 2003. LNCS, vol. 2829, pp. 91–104. Springer, Heidelberg (2003)
Gerevini, A., Saetti, A., Serina, I.: Planning through Stochastic Local Search and Temporal Action Graphs in LPG. JAIR 20, 239–290 (2003)
Haslum, P., Geffner, H.: Admissible Heuristics for Optimal Planning. In: Proc. AIPS 2000, pp. 70–82 (2000)
Helmert, M.: The Fast Downward Planning System. JAIR 26(1), 191–246 (2006)
Hoffmann, J., Nebel, B.: The FF Planning System: Fast Plan Generation Through Heuristic Search. JAIR 14, 253–302 (2001)
Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)
McDermott, D.: PDDL – The Planning Domain Definition language (1998), http://ftp.cs.yale.edu/pub/mcdermott
Muslea, I.: SINERGY: A Linear Planner Based on Genetic Programming. In: Steel, S. (ed.) ECP 1997. LNCS, vol. 1348, pp. 312–324. Springer, Heidelberg (1997)
Richter, S., Helmert, M., Westphal, M.: Landmarks Revisited. In: Proc. AAAI 2008, pp. 975–982. AAAI Press, Menlo Park (2008)
Schoenauer, M., Savéant, P., Vidal, V.: Divide-and-Evolve: a New Memetic Scheme for Domain-Independent Temporal Planning. In: Gottlieb, J., Raidl, G.R. (eds.) EvoCOP 2006. LNCS, vol. 3906, pp. 247–260. Springer, Heidelberg (2006)
Schoenauer, M., Savéant, P., Vidal, V.: Divide-and-Evolve: a Sequential Hybridization Strategy using Evolutionary Algorithms. In: Michalewicz, Z., Siarry, P. (eds.) Advances in Metaheuristics for Hard Optimization, pp. 179–198. Springer, Heidelberg (2007)
Spector, L.: Genetic Programming and AI Planning Systems. In: Proc. AAAI 1994, pp. 1329–1334. AAAI/MIT Press (1994)
Vidal, V.: A Lookahead Strategy for Heuristic Search Planning. In: 14th International Conference on Automated Planning & Scheduling - ICAPS, pp. 150–160 (2004)
Vidal, V., Geffner, H.: Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. In: Proc. AAAI, pp. 570–577 (2004)
Vidal, V., Geffner, H.: Branching and Pruning: An Optimal Temporal POCL Planner based on Constraint Programming. Artificial Intelligence 170(3), 298–335 (2006)
Westerberg, C.H., Levine, J.: “GenPlan”: Combining Genetic Programming and Planning. In: Garagnani, M. (ed.) 19th Workshop PLANSIG 2000, The Open University (2000)
Westerberg, C.H., Levine, J.: Investigations of Different Seeding Strategies in a Genetic Planner. In: Boers, E.J.W., Gottlieb, J., Lanzi, P.L., Smith, R.E., Cagnoni, S., Hart, E., Raidl, G.R., Tijink, H. (eds.) EvoIASP 2001, EvoWorkshops 2001, EvoFlight 2001, EvoSTIM 2001, EvoCOP 2001, and EvoLearn 2001. LNCS, vol. 2037, pp. 505–514. Springer, Heidelberg (2001)
Yuan, B., Gallagher, M.: Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 172–181. Springer, Heidelberg (2004)
Yuan, B., Gallagher, M.: Combining Meta-EAs and Racing for Difficult EA Parameter Tuning Tasks. In: Parameter Setting in Evolutionary Algorithms, pp. 121–142. Springer, Heidelberg (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bibai, J., Savéant, P., Schoenauer, M., Vidal, V. (2010). On the Benefit of Sub-optimality within the Divide-and-Evolve Scheme. In: Cowling, P., Merz, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2010. Lecture Notes in Computer Science, vol 6022. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12139-5_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-12139-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12138-8
Online ISBN: 978-3-642-12139-5
eBook Packages: Computer ScienceComputer Science (R0)