A stopping criterion based on Kalman estimation techniques with several progress indicators

The need for a stopping criterion in MOEA’s is a repeatedly mentioned matter in the domain of MOOP’s, even though it is usually left aside as secondary, while stopping criteria are still usually based on an a-priori chosen number of maximum iterations. In this paper we want to present a stopping criterion for MOEA’s based on three different indicators already present in the community. These indicators, some of which were originally designed for solution quality measuring (as a function of the distance to the optimal Pareto front), will be processed so they can be applied as part of a global criterion, based on estimation theory to achieve a cumulative evidence measure to be used in the stopping decision (by means of a Kalman filter). The implications of this cumulative evidence are analyzed, to get a problem and algorithm independent stopping criterion (for each individual indicator). Finally, the stopping criterion is presented from a data fusion perspective, using the differen...

• Type: Conference
• Year: 2009
• Where: GECCO
• Authors: José Luis Guerrero, Jesús García, Luis Martí, José Manuel Molina, Antonio Berlanga
• Keywords: Artificial Intelligence, GECCO 2009, Global Stopping Criterion, Independent Stopping Criterion, Stopping Criterion