PRA*: massively parallel heuristic search
Journal of Parallel and Distributed Computing
Transposition table driven work scheduling in distributed search
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Automated Planning: Theory & Practice
Automated Planning: Theory & Practice
Towards a more efficient implementation of OpenMP for clusters via translation to global arrays
Parallel Computing - OpenMp
Coarse grain parallelization of evolutionary algorithms on GPGPU cards with EASEA
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Sequential and parallel algorithms for frontier A* with delayed duplicate detection
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The deterministic part of IPC-4: an overview
Journal of Artificial Intelligence Research
Best-first heuristic search for multi-core machines
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
On the generality of parameter tuning in evolutionary planning
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The LAMA planner: guiding cost-based anytime planning with landmarks
Journal of Artificial Intelligence Research
Best-first heuristic search for multicore machines
Journal of Artificial Intelligence Research
Divide-and-Evolve: a new memetic scheme for domain-independent temporal planning
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Multicore machines are becoming a standard way to speed up the system performance. After having instantiated the evolutionary metaheuristic DAEX with the forward search YAHSP planner, we investigate on the global parallelism approach, which exploits the intrinsic parallelism of the individual evaluation. This paper describes a parallel shared-memory version of the DAEYAHSP planning system using the OpenMP directive-based API. The parallelization scheme applies at a high level of abstraction and thus can be used by any evolutionary algorithm implemented with the Evolving Objects framework. The proof of concept is validated on a 48-core machine with two planning tasks extracted from the last international planning competition. Experiments show significant speedups with an increasing number of cores. This preliminary work opens an avenue for parallelizing any evolutionary algorithm developed with EO that would target multicore architectures.