Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
On the complexity of blocks-world planning
Artificial Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Derivational Analogy in PRODIGY: Automating Case Acquisition, Storage, and Utilization
Machine Learning - Special issue on case-based reasoning
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Advances in genetic programming
Learning explanation-based search control rules for partial order planning
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Genetic programming and AI planning systems
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Formalizing the PRODIGY planning algorithm
New directions in AI planning
Lazy Incremental Learning of Control Knowledge for EfficientlyObtaining Quality Plans
Artificial Intelligence Review - Special issue on lazy learning
A comparative analysis of genetic programming
Advances in genetic programming
Inductive learning of search control rules for planning
Artificial Intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Artificial Intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming and Deductive-Inductive Learning: A Multi-Strategy Approach
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Dynamic Training Subset Selection for Supervised Learning in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Improving Graphplan's Search with EBL & DDB Techniques
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Seeding Genetic Programming Populations
Proceedings of the European Conference on Genetic Programming
SINERGY: A Linear Planner Based on Genetic Programming
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Evolving Heuristics for Planning
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Flexible reuse and modification in hierarchical planning: a validation structure-based approach
Flexible reuse and modification in hierarchical planning: a validation structure-based approach
Using multi-strategy learning techniques to improve planning efficiency and quality
Using multi-strategy learning techniques to improve planning efficiency and quality
Strongly typed genetic programming
Evolutionary Computation
Evolutionary program induction directed by logic grammars
Evolutionary Computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Search bias, language bias and genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Learning to improve both efficiency and quality of planning
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
GP-rush: using genetic programming to evolve solvers for the rush hour puzzle
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Learning action strategies for planning domains using genetic programming
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
GA-FreeCell: evolving solvers for the game of FreeCell
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms
Genetic Programming and Evolvable Machines
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Declarative problem solving, such as planning, poses interesting challenges for Genetic Programming (GP). There have been recent attempts to apply GP to planning that fit two approaches: (a) using GP to search in plan space or (b) to evolve a planner. In this article, we propose to evolve only the heuristics to make a particular planner more efficient. This approach is more feasible than (b) because it does not have to build a planner from scratch but can take advantage of already existing planning systems. It is also more efficient than (a) because once the heuristics have been evolved, they can be used to solve a whole class of different planning problems in a planning domain, instead of running GP for every new planning problem. Empirical results show that our approach (EVOCK) is able to evolve heuristics in two planning domains (the blocks world and the logistics domain) that improve PRODIGY4.0 performance. Additionally, we experiment with a new genetic operator - Instance-Based Crossover - that is able to use traces of the base planner as raw genetic material to be injected into the evolving population.