16 citations · 31 across the 6 of their papers we have counts for
6 papers
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…
Scalability of Genetic Programming and Probabilistic Incremental Program Evolution
Radovan Ondas, Martin Pelikan, Kumara Sastry
This paper discusses scalability of standard genetic programming (GP) and the probabilistic incremental program evolution (PIPE). To investigate the need for both effective mixing…
Parallel Mixed Bayesian Optimization Algorithm: A Scaleup Analysis
Jiri Ocenasek, Martin Pelikan
Estimation of Distribution Algorithms have been proposed as a new paradigm for evolutionary optimization. This paper focuses on the parallelization of Estimation of Distribution Al…
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…
Parameter-less hierarchical BOA
Martin Pelikan, Tz-Kai Lin
The parameter-less hierarchical Bayesian optimization algorithm (hBOA) enables the use of hBOA without the need for tuning parameters for solving each problem instance. There are t…