<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Experimental design is concerned with the problem of allocating resources within an experiment to ensure that objectives of the experiment are achieved at the minimum cost. This paper focuses on the generation of optimal or near-optimal designs for large and complex experiments where it is infeasible to carry out an ex- haustive search of the design space. Optimal designs for gene expression studies, aimed at investigating the behaviour of genes, are considered, where the optimality criterion employed is Pareto optimality. We develop an adaptation of the metaheuris- tic method of Pareto simulated annealing to generate an approximation to the set of Pareto optimal designs for large and complex experiments. We develop algorithms that utilise response surface methodology to search systematically for the optimal values of parameters associated with Pareto simulated annealing and performance is evaluated using quality measures.</p></abstract>

Experimental design is concerned with the problem of allocating resources within an experiment to ensure that objectives of the experiment are achieved at the minimum cost. This paper focuses on the generation of optimal or near-optimal designs for large and complex experiments where it is infeasible to carry out an ex- haustive search of the design space. Optimal designs for gene expression studies, aimed at investigating the behaviour of genes, are considered, where the optimality criterion employed is Pareto optimality. We develop an adaptation of the metaheuris- tic method of Pareto simulated annealing to generate an approximation to the set of Pareto optimal designs for large and complex experiments. We develop algorithms that utilise response surface methodology to search systematically for the optimal values of parameters associated with Pareto simulated annealing and performance is evaluated using quality measures.

Pareto simulated annealing for the design of experiments: illustrated by a gene expression study

University of South Australia, School of Mathematics and Statistics, Mawson Lakes Campus, Mawson Lakes, South Australia, 5095, Australia

University of Adelaide, School of Mathematical Sciences, Adelaide, South Australia, 5005, Australia

Foundations of Computing and Decision Sciences

Experimental design is concerned with the problem of allocating resources within an experiment to ensure that objectives of the experiment are achieved at the...

Pareto simulated annealing for the design of experiments: illustrated by a gene expression study

Published Online: Oct 01, 2012

Page range: 199 - 221

DOI: https://doi.org/10.2478/v10209-011-0011-z

Keywords
Combinatorial optimisation, Metaheuristics, Pareto simulated annealing, Experimental design, Gene expression study

This content is open access.

Pareto simulated annealing for the design of experiments: illustrated by a gene expression study

Published Online: Oct 01, 2012

Page range: 199 - 221

DOI: https://doi.org/10.2478/v10209-011-0011-z

KeywordsCombinatorial optimisation, Metaheuristics, Pareto simulated annealing, Experimental design, Gene expression study

This content is open access.

Keywords
Combinatorial optimisation, Metaheuristics, Pareto simulated annealing, Experimental design, Gene expression study