Planning as search: a quantitative approach
Artificial Intelligence
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Genetic programming and AI planning systems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Fitness landscapes and memetic algorithm design
New ideas in optimization
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Temporal Planning with Mutual Exclusion Reasoning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Perspectives on artificial intelligence planning
Eighteenth national conference on Artificial intelligence
Fox-ga: A genetic algorithm for generating and analyzing battlefield courses of action
Evolutionary Computation
Branching and pruning: an optimal temporal POCL planner based on constraint programming
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
On reasonable and forced goal orderings and their use in an agenda-driven planning algorithm
Journal of Artificial Intelligence Research
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Using performance fronts for parameter setting of stochastic metaheuristics
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
On the generality of parameter tuning in evolutionary planning
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Parallel divide-and-evolve: experiments with OpenMP on a multicore machine
Proceedings of the 13th 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
On the benefit of sub-optimality within the divide-and-evolve scheme
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
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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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.