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
A Racing Algorithm for Configuring Metaheuristics
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
SINERGY: A Linear Planner Based on Genetic Programming
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
The Journal of Machine Learning Research
Costs and Benefits of Tuning Parameters of Evolutionary Algorithms
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Parameter Setting in Evolutionary Algorithms
Parameter Setting in Evolutionary Algorithms
PDDL2.1: an extension to PDDL for expressing temporal planning domains
Journal of Artificial Intelligence Research
Evolutionary Computation in Practice
Evolutionary Computation in Practice
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
ParamILS: an automatic algorithm configuration framework
Journal of Artificial Intelligence Research
On the generality of parameter tuning in evolutionary planning
Proceedings of the 12th annual conference 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
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
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Learn-and-Optimize (LaO) is a generic surrogate based method for parameter tuning combining learning and optimization. In this paper LaO is used to tune Divide-and-Evolve (DaE), an Evolutionary Algorithm for AI Planning. The LaO framework makes it possible to learn the relation between some features describing a given instance and the optimal parameters for this instance, thus it enables to extrapolate this relation to unknown instances in the same domain. Moreover, the learned knowledge is used as a surrogate-model to accelerate the search for the optimal parameters. The proposed implementation of LaO uses an Artificial Neural Network for learning the mapping between features and optimal parameters, and the Covariance Matrix Adaptation Evolution Strategy for optimization. Results demonstrate that LaO is capable of improving the quality of the DaE results even with only a few iterations. The main limitation of the DaE case-study is the limited amount of meaningful features that are available to describe the instances. However, the learned model reaches almost the same performance on the test instances, which means that it is capable of generalization.