16 citations · 43 across the 7 of their papers we have counts for
6 papers · 1 filter
Decomposable Problems, Niching, and Scalability of Multiobjective Estimation of Distribution Algorithms
Kumara Sastry, Martin Pelikan, David E. Goldberg
The paper analyzes the scalability of multiobjective estimation of distribution algorithms (MOEDAs) on a class of boundedly-difficult additively-separable multiobjective optimizati…
Multiobjective hBOA, Clustering, and Scalability
Martin Pelikan, Kumara Sastry, David E. Goldberg
This paper describes a scalable algorithm for solving multiobjective decomposable problems by combining the hierarchical Bayesian optimization algorithm (hBOA) with the nondominate…
Sub-structural Niching in Estimation of Distribution Algorithms
K. Sastry, H. A. Abbass, D. E. Goldberg +1
We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms an…
Oiling the Wheels of Change: The Role of Adaptive Automatic Problem Decomposition in Non--Stationary Environments
H. A. Abbass, K. Sastry, D. E. Goldberg
Genetic algorithms (GAs) that solve hard problems quickly, reliably and accurately are called competent GAs. When the fitness landscape of a problem changes overtime, the problem i…
Designing Competent Mutation Operators via Probabilistic Model Building of Neighborhoods
Kumara Sastry, David E. Goldberg
This paper presents a competent selectomutative genetic algorithm (GA), that adapts linkage and solves hard problems quickly, reliably, and accurately. A probabilistic model buildi…
Efficiency Enhancement of Probabilistic Model Building Genetic Algorithms
Kumara Sastry, David E. Goldberg, Martin Pelikan
This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator…