Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012

A Rank Approach to Equity Forecast Construction

The purpose of this paper is to present a rank based approach to cross-sectionallinear factor modelling. The emphasis is on approximating factor exposures in aconsistent manner in order to facilitate the merging of subjective information(from professional investors) with objective information (from accounting dataand/or state of the art quantitative models) in a statistically rigorous way withoutneeding to impose the unrealistic simplifying assumptions typical of more standardtime series models. We deal with the problems of identifying country and sectorreturns by an innovative hierarchical factor structure. This is all discussed fromthe perspective that investment models are not immutable but rather need to bedesigned with characteristics that are fit for their purpose; for example, returningaggregate county and sector forecasts that are consistent by construction

Ranking and Selection under Input Uncertainty: Fixed Confidence and Fixed Budget

In stochastic simulation, input uncertainty (IU) is caused by the error in estimating the input distributions using finite real-world data. When it comes to simulation-based Ranking and Selection (R&S), ignoring IU could lead to the failure of many existing selection procedures. In this paper, we study R&S under IU by allowing the possibility of acquiring additional data. Two classical R&S formulations are extended to account for IU: (i) for fixed confidence, we consider when data arrive sequentially so that IU can be reduced over time; (ii) for fixed budget, a joint budget is assumed to be available for both collecting input data and running simulations. New procedures are proposed for each formulation using the frameworks of Sequential Elimination and Optimal Computing Budget Allocation, with theoretical guarantees provided accordingly (e.g., upper bound on the expected running time and finite-sample bound on the probability of false selection). Numerical results demonstrate the effectiveness of our procedures through a multi-stage production-inventory problem

A framework for the selection of the right nuclear power plant

Civil nuclear reactors are used for the production of electrical energy. In the nuclear industry vendors propose several nuclear reactor designs with a size from 35–45 MWe up to 1600–1700 MWe. The choice of the right design is a multidimensional problem since a utility has to include not only financial factors as levelised cost of electricity (LCOE) and internal rate of return (IRR), but also the so called “external factors” like the required spinning reserve, the impact on local industry and the social acceptability. Therefore it is necessary to balance advantages and disadvantages of each design during the entire life cycle of the plant, usually 40–60 years. In the scientific literature there are several techniques for solving this multidimensional problem. Unfortunately it does not seem possible to apply these methodologies as they are, since the problem is too complex and it is difficult to provide consistent and trustworthy expert judgments. This paper fills the gap, proposing a two-step framework to choosing the best nuclear reactor at the pre-feasibility study phase. The paper shows in detail how to use the methodology, comparing the choice of a small-medium reactor (SMR) with a large reactor (LR), characterised, according to the International Atomic Energy Agency (2006), by an electrical output respectively lower and higher than 700 MWe

An analysis of some mistakes, miracles and myths in supplier selection

This paper analyzes some consequences of formal methods and procedures for supplier selection. It argues that many mistakes and miracles may occur in frequently used procedures. Practical examples are given. In the analysis it turns out that preventing these unwanted effects from occurring may be tackled by methodological improvements. Some examples and guidelines for these are given as well. But another important point lies in the perspectives of the actors in supplier selection: governments and industry policy makers, purchasers, suppliers and (management) researchers. The analysis shows that these different actors often operate from quite different and sometimes conflicting attitudes, assumptions and principles. On the one hand this analysis leads to the conclusion that using some sort of formal approach for supplier selection may be necessary. On the other hand it clarifies the criticism on such an approach and the difficulties associated with its use. The paper concludes with recommendations and implications for policy makers, researchers, and practitioners