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Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob J. Hyndman, Kate Smith-Miles and Laura Villanova

In Advances in Stochastic and Deterministic Global Optimization, ed. P.M. Pardalos, A. Zhigljavsky, J. Žilinskas. Springer (2016), pp. 273–296.

Abstract
We investigate the impact of different sampling techniques on the performance of multi-objective optimization methods applied to costly black-box optimization problems. Such problems are often solved using an algorithm in which a surrogate model approximates the true objective function and provides predicted objective values at a lower cost. As the surrogate model is based on evaluations of a small number of points, the quality of the initial sample can have a great effect on the overall effectiveness of the optimization. In this study, we demonstrate how various sampling techniques affect the results of applying different optimization algorithms to a set of benchmark problems. Additionally, some recommendations on usage of sampling methods are provided.

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  Tag: optimization

6 posts
February 29th, 2016

On sampling methods for costly multi-objective black-box optimization

Ingrida Steponavičė, Mojdeh Shirazi-Manesh, Rob J. Hyndman, Kate Smith-Miles and Laura Villanova In Advances in Stochastic and Deterministic Global Optimization, […]

February 19th, 2016

Dynamic Algorithm Selection for Pareto Optimal Set Approximation

Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles, Laura Villanova Journal of Global Optimization (2016), pp.1-20. Abstract: This paper presents a meta-algorithm […]

November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

Shanika L Wickramasuriya, George Athanasopoulos, Rob J Hyndman Department of Econometrics and Business Statistics, Monash University   Abstract Large collections […]

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

By Alex Dokumentov and Rob J Hyndman

August 1st, 2014

Efficient identification of the Pareto optimal set

By Ingrida Steponavičė, Rob J Hyndman, Kate Smith-Miles and Laura Villanova

Learning and Intelligent Optimization.
Lecture Notes in Computer Science, vol 8426, 341-352.

June 5th, 2014

Low-dimensional decomposition, smoothing and forecasting of sparse functional data

By Alexander Dokumentov and Rob J Hyndman

October 31st, 2013

Nonparametric and semiparametric response surface methodology: a review of designs, models and optimization techniques

Laura Villanova, Rob J Hyndman, Kate Smith-Miles, Irene Poli Abstract: Since the introduction of Response Surface Methodology in the 1950s, there […]

July 14th, 2011

Method for optimizing coating properties based on an evolutionary algorithm approach

Davide Carta, Laura Villanova, Stefano Costacurta, Alessandro Patelli, Irene Poli, Simone Vezzu, Paolo Scopece, Fabio Lisi, Kate Smith-Miles, Rob J […]

February 8th, 2010

Functionalization of microarray devices: process optimization using a multiobjective PSO and multiresponse MARS modeling

L. Villanova, P. Falcaro, D. Carta, I. Poli, R. J. Hyndman, K. Smith-Miles 2010 IEEE Congress on Evolutionary Computation, July […]