Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming and AI planning systems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Investigation of Different Seeding Strategies in a Genetic Planner
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
SINERGY: A Linear Planner Based on Genetic Programming
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Branching and pruning: an optimal temporal POCL planner based on constraint programming
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'08 Proceedings of the 23rd 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
Planning through stochastic local search and temporal action graphs in LPG
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
Branching and pruning: An optimal temporal POCL planner based on constraint programming
Artificial Intelligence
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
On the generality of parameter tuning in evolutionary planning
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Instance-based parameter tuning for evolutionary AI planning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Instance-based parameter tuning for evolutionary AI planning
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Learn-and-Optimize: a parameter tuning framework for evolutionary AI planning
EA'11 Proceedings of the 10th international conference on Artificial Evolution
Multi-objective AI planning: comparing aggregation and pareto approaches
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Pareto-based multiobjective AI planning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.