Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Machine learning is inherently a multiobjective task. Traditionally, however, either only one
of the objectives is adopted as the cost function or multiple objectives are aggregated to a …

In credit scoring, feature selection aims at removing irrelevant data to improve the
performance of the scorecard and its interpretability. Standard techniques treat feature …

Electric traction motors in automotive applications work in operational conditions
characterized by variable load, rotational speed and other external conditions: this …

Feature selection is a common and key problem in many classification and regression tasks.
It can be viewed as a multiobjective optimisation problem, since, in the simplest case, it …

To assist readers to have a comprehensive understanding, the classical and intelligent
methods roundly based on precursory research achievements are summarized in this paper …

Fault diagnostic methods are challenged by their applications to industrial components
operating in evolving environments of their working conditions. To overcome this problem …

List of References on Evolutionary Multiobjective Optimization Page 1 List of References on
Evolutionary Multiobjective Optimization Carlos A. Coello Coello ccoello@cs.cinvestav.mx …

The issue of feature selection is particularly critical for the application of monitoring and" on
condition" diagnostic techniques to complex plants, like the nuclear power plants, where …

In this paper, a multi-layer perceptron (MLP) model and the Finnish variant of the numerical
weather prediction model HIRLAM (High Resolution Limited Area Model) were integrated …