Heuristics for Optimising the Calculation of Hypervolume for Multi-objective Optimisation Problems

The fastest known algorithm for calculating the hypervolume of a set of solutions to a multi-objective optimization problem is the HSO algorithm (hypervolume by slicing objectives). However, the performance of HSO for a given front varies a lot depending on the order in which it processes the objectives in that front. We present and evaluate two alternative heuristics that each attempt to identify a good order for processing the objectives of a given front. We show that both heuristics make a substantial difference to the performance of HSO for randomly-generated and benchmark data in 5-9 objectives, and that they both enable HSO to reliably avoid the worst-case performance for those fronts. The enhanced HSO enable the use of hypervolume with larger populations in more objectives

Map-labelling with a Multi-objective Evolutionary Algorithm ABSTRACT

We present a multi-objective evolutionary algorithm approach to the map-labelling problem. Map-labelling involves placing labels for sites onto a map such that the result is easy to read and usable for navigation. However, map-users vary in their priorities and capabilities: for example, sightimpaired users need to maximise font-size, whereas other users may be willing to accept smaller labels in exchange for increased clarity of bindings of labels to sites. With a multiobjective approach, we evolve a range of labellings from which users can select according to their particular circumstances. We present results from labelling two maps, including a difficult, dense map of Newcastle County in Delaware, which clearly illustrate the advantages of the multi-objective approach

Heuristincs for Optimising the Calculation of Hypervolume for Multi-objective Optimisation Problems

Abstract- The fastest known algorithm for calculating the hypervolume of a set of solutions to a multiobjective optimisation problem is the HSO algorithm (Hypervolume by Slicing Objectives). However, the performance of HSO for a given front varies a lot depending on the order in which it processes the objectives in that front. We present and evaluate two alternative heuristics that each attempt to identify a good order for processing the objectives of a given front. We show that both heuristics make a substantial difference to the performance of HSO for randomly-generated and benchmark data in 5–9 objectives, and that they both enable HSO to reliably avoid the worst-case performance for those fronts. The enhanced HSO will enable the use of hypervolume with larger populations in more objectives.

Etude comparative des critères de qualité de deux trousses de dosage de thyroglobuline (THYRO (Schering CIS bio®) et Access 2 (Beckman Coutler®) pour le suivi des cancers thyroïdiens différenciés)

LYON1-BU Santé (693882101) / SudocSudocFranceF

Business and Default Cycles for Credit Risk

Various economic theories are available to explain the existence of credit and default cycles. There remains empirical ambiguity, however, as to whether these cycles coincide. Recent papers suggest by their empirical research set-up that they do, or at least that defaults and credit spreads tend to co-move with macroeconomic variables. If true, this is important for credit risk management as well as for regulation and systemic risk management. In this paper, we use 1933-1997 US data on real GDP, credit spreads and business failure rates to shed new light on the empirical evidence. We use a multivariate unobserved components framework to disentangle credit and business cycles. We distinguish two types of cycles in the data, corresponding to periods of around 6 and 11-16 years, respectively. Cyclical co-movements between GDP and business failures mainly arise at the longer frequency. At the higher frequency of 6 years, co-cyclicality is less clear-cut. We also show that spreads reveal a positive and negative co-cyclicality with failure rates and GDP, respectively. This pattern disappears, however, if we concentrate on the post World War II period. We comment on the implications of our findings for credit risk management. Copyright © 2005 John Wiley & Sons, Ltd

Business failures and macroeconomic factors in the UK

We examine the interactions between business failures and macroeconomic aggregates, and specifically the accounts of policy-induced changes in the macroeconomy for the observed fluctuations of UK business failures in the period 1966–2003 using the vector errorcorrection model (VECM). The results demonstrate that macroeconomic aggregates, i.e., interest rate, credit, profits, inflation and business births, exert differential impacts on business failures both in the short run and in the long run. The study reveals that structural changes in the financial and real sectors during the examined period have made an impact on the way in which the macroeconomy affects business failures. In particular, business failures are increasingly reacting tomonetary policy changes in the post-1980 period. Furthermore, the shocks to business failures can generate large fluctuations in macroeconomic aggregates, suggesting the importance of corporate balance sheets in financial stability and economic growth. The paper’s findings carry policy implications that are related to the survival of firms in distress and finance-driven business cycles