EMO 2013 - 7th International Conference on Evolutionary Multi-Criterion Optimization

The 7th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2013) will be hosted in Sheffield, UK, from 19-22 March 2013.
EMO 2013 aims to build on the success of the 6th meeting in Ouro Preto, Brazil, in bringing together the EMO and multiple criteria decision making (MCDM) communities, and will also stimulate a new focus on the application of EMO and MCDM research to help solve real problems in government, business and industry.
Conference format
The conference will include:
Invited keynote and tutorial speakers
Core EMO track
MCDM track
Real-world applications track
Following the research family tradition of the EMO community, the conference will be in single session format, with high-quality, peer-reviewed papers delivered as either oral presentations or as interactive poster presentations.
Publication
All accepted works will be published as full papers in Springer-Verlag’s Lecture Notes in Computer Science (LNCS) series (maximum 15 pages; templates and further guidance to follow).
Core EMO track
In the core EMO track, full papers are invited on novel aspects of EMO theory and methodology. Topics may include, but are not limited to:
New developments for existing classes of EMO algorithms: e.g. aggregation- based, dominance-based, indicator-based
Methods for a priori, progressive and a posteriori knowledge mining of the problem landscape
Methods to handle problems with more than three objectives
Methods to handle problems featuring uncertainty in decision-space and/or objective-space
Methods to handle expensive objective function evaluations
Methods to handle dynamic problems
Methods for interactive preference articulation and exploitation, including consideration of human biases
Theoretical analysis of EMO algorithms
New developments in collaborative and parallel EMO approaches: e.g. algorithm portfolios, divide-and-conquer methods, use of parallel and distributed hardware
New approaches to solving different multi-criterion problem classes: e.g. network topology problems, assignment problems, multidisciplinary optimization problems
New developments in related paradigms, e.g. ant colony optimization, particle swarm optimization, differential evolution, artificial immune systems, estimation of distribution algorithms, variable neighbourhood search, iterated local search, simulated annealing, Tabu search
New paradigms for population-based multi-criterion optimization
New benchmark problems, performance indicators, and methods for empirical analysis of EMO algorithms
Hybrid EMO-MCDM methodologies.