The sciences of the artificial (3rd ed.)
The sciences of the artificial (3rd ed.)
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
Learning gene linkage to efficiently solve problems of bounded difficulty using genetic algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
RapidAccurate Optimization of Difficult Problems Using Fast Messy Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Variable discrimination of crossover versus mutation using parameterized modular structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Functional modularity for genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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We describe a generator for hierarchical problems called the Hierarchical Problem Generator (HPG). Hierarchical problems are of interest since they constitute a class of problems that can be addressed efficiently, even though high-order dependencies between variables may exist. The generator spans a wide ranges of hierarchical problems, and is limited to producing hierarchical problems. It is therefore expected to be useful in the study of hierarchical methods, as has already been demonstrated in experiments. The generator is freely available for research use.