On the computation of the Empirical Attainment Function

The attainment function provides a description of the location of the distribution of a random non-dominated point set. This function can be estimated from experimental data via its empirical counterpart, the empirical attainment function (EAF). However, computation of the EAF in more than two dimensions is a non-trivial task. In this article, the problem of computing the empirical attainment function is formalised, and upper and lower bounds on the corresponding number of output points are presented. In addition, efficient algorithms for the two and three-dimensional cases are proposed, and their time complexities are related to lower bounds derived for each case. © 2011 Springer-Verlag.R. H. C. Takahashi et al. editors, Evolutionary Multi-criterion Optimization (EMO 2011)SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Exploratory Analysis of Stochastic Local Search Algorithms in Biobjective Optimization

This chapter introduces two Perl programs that implement graphical tools for exploring the performance of stochastic local search algorithms for biobjective optimization problems. These tools are based on the concept of the empirical attainment function (EAF), which describes the probabilistic distribution of the outcomes obtained by a stochastic algorithm in the objective space. In particular, we consider the visualization of attainment surfaces and differences between the first-order EAFs of the outcomes of two algorithms. This visualization allows us to identify certain algorithmic behaviors in a graphical way. We explain the use of these visualization tools and illustrate them with examples arising from practice. © 2010 Springer-Verlag Berlin Heidelberg.SCOPUS: ch.binfo:eu-repo/semantics/publishe

Distortion in statistical inference: the distinction between data contamination and model deviation

Distortion, Data contamination, Model deviation, Classical statistical inference, Inferential performance,

Effective hybrid stochastic local search algorithms for biobjective permutation flowshop scheduling

SCOPUS: cp.kinfo:eu-repo/semantics/publishe

Control Theoretical Challenges in Systems Biology

In M. Dorigo, L. Gambardella, F. Mondada, T. St¨utzle, M. Birratari, and C. Blum, editors, ANTS’2004, Fourth International Workshop on Ant Algorithms and Swarm Intelligence, Springer Verlag, Berlin, Germany.info:eu-repo/semantics/publishe