JOSE maia NEVES

Prof. Dr. JOSE maia NEVES

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Research Focus & Metrics

Academic Career Length

45Years (1982-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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14 June 2004 in IEEE Transactions on Evolutionary Computation
Verified
Journal Article
Closed access
RMR. Mendes
R. Mendes
JKJ. Kennedy
J. Kennedy
JNJOSE maia NEVES
JOSE maia NEVES
The canonical particle swarm algorithm is a new approach to optimization, drawing inspiration from group behavior and the establishment of social norms. It is gaining popularity, especially because of the speed of convergence and the fact that it is easy to use. However, we feel that each individual is not simply influenced by the best performer among his neighbors. We, thus, decided to make the individuals "fully informed." The results are very promising, as informed individuals seem to find better solutions in all the benchmark functions.

25 June 2003
Verified
Conference Paper
Closed access
RMR. Mendes
R. Mendes
PCPaulo Cortez
Paulo Cortez
Miguel RochaMiguel Rocha
Miguel Rocha
JNJOSE maia NEVES
JOSE maia NEVES

1 December 2012 in Expert Systems with Applications
Verified
Journal Article
Closed access
Davide CarneiroDavide Carneiro
Davide Carneiro
José Carlos CastilloJosé Carlos Castillo
José Carlos Castillo
Paulo NovaisPaulo Novais
Paulo Novais
Antonio Fernández-CaballeroAntonio Fernández-Caballero
Antonio Fernández-Caballero
JNJOSE maia NEVES
JOSE maia NEVES

1 October 2007 in Neurocomputing
Verified
Journal Article
Closed access
Miguel RochaMiguel Rocha
Miguel Rocha
PCPaulo Cortez
Paulo Cortez
JNJOSE maia NEVES
JOSE maia NEVES

22 March 2004
Verified
Conference Paper
Closed access
RMR. Mendes
R. Mendes
JKJ. Kennedy
J. Kennedy
JNJOSE maia NEVES
JOSE maia NEVES
Particle swarm optimization is a novel algorithm where a population of candidate problem solution vectors evolves "social" norms by being influenced by their topological neighbors. Until now, an individual was influenced by its best performance acquired in the past and the best experience observed in its neighborhood. In this paper, we introduce new ways an individual can be influenced by its neighbors.

3 January 2012 in Artificial Intelligence Review
Verified
Journal Article
Open access
PDF file
Davide CarneiroDavide Carneiro
Davide Carneiro
Paulo NovaisPaulo Novais
Paulo Novais
FAFrancisco Andrade
Francisco Andrade
JZJohn Zeleznikow
John Zeleznikow
JNJOSE maia NEVES
JOSE maia NEVES
Litigation in court is still the main dispute resolution mode. However, given the amount and characteristics of the new disputes, mostly arising out of electronic contracting, courts are becoming slower and outdated. Online Dispute Resolution (ODR) recently emerged as a set of tools and techniques, supported by technology, aimed at facilitating conflict resolution. In this paper we present a critical evaluation on the use of Artificial Intelligence (AI) based techniques in ODR. In order to fulfill this goal, we analyze a set of commercial providers (in this case twenty four) and some research projects (in this circumstance six). Supported by the results so far achieved, a new approach to deal with the problem of ODR is proposed, in which we take on some of the problems identified in the current state of the art in linking ODR and AI.