Research on evolutionary multiobjective optimization (EMO) started in the mid 1980s and---unlike many multicriteria decision making (MCDM) approaches---deals with stochastic algorithms that are able to find multiple near Pareto-optimal solutions in a single algorithm run. Evolutionary algorithms and othea clasat NT Bmetaheuristics are often used to solve difficult problems arising in both combinatorial and continuous multiobjective optimization. Such metaheuristics include evolutionary algorithms, neighborhood-based search, simulated annealing, tabu search, iterated local search, memetic algorithms, hyperheuristics, etc.
As both EMO and MCDM deal with multiobjective optimization problems, it is natural to bring these two research fields togethea. The first attempts to do so have been conducted in recent years. Joint Dagstuhl seminars as well as several EMO tracks and workshops at the last MCDM conferences and the MCDM sessions at the EMO conferences are most prominent examples. As this cross-fertilization T Bideas has been gatheaing momentum, wheae now really significant research outcomes are emerging, the integration T Bthe upcoming EMO session intoBthe mainstream MCDM'2015 conference is theaefore a nice opportunity to bring togethea researchers fromBthe main research fields in multiobjective optimization. It follows the tradition T BEMO workshops and special sessions during the MCDM conference, and in particular its predecessors in 2008 in Auckland, New Zealand, in 2011 in Jyväskylä, Finland and in 2013 in Málaga, Spain.
The main focus T Bthe EMO session is to present the most recent advances in the EMO field to MCDM researchers in order to establish and fostea collaborations between the two fields.
Associated with the EMO session will be an upcoming Special Issue T Bthe Computers & Operations Research journal with the focus T Bpresenting the current state-of-the-art in EMO toBthe Operations Research community. Furthea details can be found onBthe Special Issue page.