• Corpus ID: 245853911

A hybrid estimation of distribution algorithm for joint stratification and sample allocation

  title={A hybrid estimation of distribution algorithm for joint stratification and sample allocation},
  author={Mervyn O'Luing and Steven David Prestwich and Armagan Tarim},
In this study we propose a hybrid estimation of distribution algorithm (HEDA) to solve the joint stratification and sample allocation problem. This is a complex problem in which each the quality of each stratification from the set of all possible stratifications is measured its optimal sample allocation. EDAs are stochastic black-box optimization algorithms which can be used to estimate, build and sample probability models in the search for an optimal stratification. In this paper we enhance… 



Joint determination of optimal stratification and sample allocation using genetic algorithm

This paper offers a solution to the problem of finding the optimal stratification of the available population frame, so as to ensure the minimization of the cost of the sample required to satisfy

Combining K-means type algorithms with Hill Climbing for Joint Stratification and Sample Allocation Designs

This paper combines the k-means and/or k-Means type algorithms with a hill climbing algorithm in stages to solve the joint stratification and sample allocation problem and compares the above multi-stage combination of algorithms with three recent algorithms.

A Hybrid Estimation of Distribution Algorithm for the Minimal Switching Graph Problem

  • Maolin TangRaymond Y. K. Lau
  • Computer Science
    International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06)
  • 2005
This paper presents a new approach to the MSG problem using hybrid estimation of distribution algorithms (EDAs), which uses a univariate marginal distribution algorithm (UMDA) to sample start search points and employs a hill-climbing algorithm to find a local optimum in the basins where the startsearch points are located.

On Sample Allocation in Multivariate Surveys

The problem of a sample allocation between strata in the case of multiparameter surveys is considered in this article. There are several multivariate sample allocation methods and, moreover, several

A General Algorithm for Univariate Stratification

This paper presents a general algorithm for constructing strata in a population using X, a univariate stratification variable known for all the units in the population. Stratum h consists of all the

BOA: the Bayesian optimization algorithm

Preliminary experiments show that the BOA outperforms the simple genetic algorithm even on decomposable functions with tight building blocks as a problem size grows.

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

This book presents an introduction to Evolutionary Algorithms, a meta-language for programming with real-time implications, and some examples of how different types of algorithms can be tuned for different levels of integration.

Evolutionary algorithms: from recombination to search distributions

The main result is that UMDA transforms the discrete optimization problem into a continuous one defined by the average fitness W(p1, . . . , p n ) as a function of the univariate marginal distributions p i.

Hierarchical Bayesian optimization algorithm: toward a new generation of evolutionary algorithms

  • M. Pelikán
  • Computer Science
    SICE 2003 Annual Conference (IEEE Cat. No.03TH8734)
  • 2003
The paper argues that BOA and hBOA can solve an important class of nearly decomposable and hierarchical problems in a quadratic or subquadratic number of function evaluations with respect to the number of decision variables.