Thanks to their inherent properties, probabilistic graphical models are one of the prime
candidates for machine learning and decision making tasks especially in uncertain domains …

The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) into the
market brings up many technical problems that need to be addressed. In the near future, a …

In recent decades, Estimation of Distribution Algorithms (EDAs) have gained much
popularity in the evolutionary computation community for solving optimization problems …

The variety of today's architectures forces programmers to spend a great deal of time porting
and tuning application codes across different platforms. Compilers themselves need …

Objective Is it possible to predict the severity staging of a Parkinson's disease (PD) patient
using scores of non-motor symptoms? This is the kickoff question for a machine learning …

This paper proposes a new multiobjective estimation of distribution algorithm (EDA) based
on joint probabilistic modeling of objectives and variables. This EDA uses the …

Estimation distribution algorithm (EDA) is an evolution technique that uses sampling to
generate the offspring. Most developed EDAs focus on solving the optimization problems …

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

Internet resources available today, including songs, albums, playlists or podcasts, that a
user cannot discover if there is not a tool to filter the items that the user might consider …

The Mallows (MM) and the Generalized Mallows (GMM) probability models have
demonstrated their validity in the framework of Estimation of distribution algorithms (EDAs) …