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
A generator for hierarchical problems
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Fda -a scalable evolutionary algorithm for the optimization of additively decomposed functions
Evolutionary Computation
A generator for hierarchical problems
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Factorial representations to generate arbitrary search distributions
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
gLINC: identifying composability using group perturbation
Proceedings of the 8th annual conference on Genetic and evolutionary computation
How an optimal observer can collapse the search space
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Linkage identification by fitness difference clustering
Evolutionary Computation
Towards memoryless model building
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Functional modularity for genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
On the detection of general problem structures by using inductive linkage identification
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Symbiosis, synergy and modularity: introducing the reciprocal synergy symbiosis algorithm
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
A surrogate-assisted linkage inference approach in genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Symbiosis enables the evolution of rare complexes in structured environments
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
Symbiogenesis as a mechanism for building complex adaptive systems: a review
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Linkage learning by number of function evaluations estimation: Practical view of building blocks
Information Sciences: an International Journal
Hierarchical problem solving with the linkage tree genetic algorithm
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Self-focusing genetic programming for software optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Competent Genetic Algorithms can efficiently address problems in which the linkage between variables is limited to a small order k. Problems with higher order dependencies can only be addressed efficiently if further problem properties exist that can be exploited. An important class of problems for which this occurs is that of hierarchical problems. Hierarchical problems can contain dependencies between all variables (k=n) while being solvable in polynomial time.An open question so far is what precise properties a hierarchical problem must possess in order to be solvable efficiently. We study this question by investigating several features of hierarchical problems and determining their effect on computational complexity, both analytically and empirically. The analyses are based on the Hierarchical Genetic Algorithm (HGA), which is developed as part of this work. The HGA is tested on ranges of hierarchical problems, produced by a generator for hierarchical problems.