Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
A Survey of Optimization by Building and Using Probabilistic Models
Computational Optimization and Applications
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Space Complexity of Estimation of Distribution Algorithms
Evolutionary Computation
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
On the treewidth of NK landscapes
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Analyzing probabilistic models in hierarchical BOA on traps and spin glasses
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Enhancing Efficiency of Hierarchical BOA Via Distance-Based Model Restrictions
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Analyzing probabilistic models in hierarchical BOA
IEEE Transactions on Evolutionary Computation - Special issue on evolutionary algorithms based on probabilistic models
NK landscapes, problem difficulty, and hybrid evolutionary algorithms
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Network crossover performance on NK landscapes and deceptive problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The linkage tree genetic algorithm
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Performance of network crossover on NK landscapes and spin glasses
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Advanced neighborhoods and problem difficulty measures
Proceedings of the 13th annual conference on Genetic and evolutionary computation
The lay of the land: a brief survey of problem understanding
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Learning the neighborhood with the linkage tree genetic algorithm
LION'12 Proceedings of the 6th international conference on Learning and Intelligent Optimization
| Hi-index | 0.00 |
This paper describes a class of random additively decomposable problems (rADPs) with and without interactions between the subproblems. The paper then tests the hierarchical Bayesian optimization algorithm (hBOA) and other evolutionary algorithms on a large number of random instances of the proposed class of problems. The results show that hBOA can scalably solve rADPs and that it significantly outperforms all other methods included in the comparison. Furthermore, the results provide a number of interesting insights into both the difficulty of a broad class of decomposable problems as well as the sensitivity of various evolutionary algorithms to different sources of problem difficulty. rADPs can be used to test other optimization algorithms.