[alife] Special Session on Evolutionary Multi-objective Machine Learning

[Ricardo Aler]

Special Session on Evolutionary Multi-objective Machine Learning Description

Many current research works have combined the global search abilities of
Evolutionary Computation with Machine Learning algorithms. Most of these
hybrid approaches use mono-objective fitness functions. However, many issues
in Machine Learning are multi-objective in nature. For instance, in feature
selection, the minimization of the number of attributes and the maximization
of accuracy are conflicting goals. Also, new powerful multi-objective
optimization algotithms have been developed. That is why recently,
multi-objective approaches have been applied to Machine Learning problems
such as: improving the generalization capabilities of learning algorithms,
generating diverse classifiers for building ensembles, reducing the
complexity of models for improving interpretability, multi-objective-based
feature selection, clustering, etc. This special session welcomes articles
on advances on evolutionary multi-objective-based Machine Learning. Papers
comparing and studying the advantages and disadvantages of the
multi-objective versus the mono-objective approach are also welcome.

Topics include but are not limited to:

   - Evolutionary multi-objective techniques for improving the
   generalization capabilities of machine learning algorithms
   - Evolutionary multi-objective techniques for improving interpretability
   of models
   - Evolutionary multi-objective feature selection
   - Evolutionary multi-objective ensemble generation
   - Empirical and/or theoretical comparisons between evolutionary
   mono-objective and multi-objective machine learning techniques
   - Multi-objective Genetic Programming
   - New evolutionary multi-objective algorithms speciallized in machine
   learning
   - Applications of evolutionary multi-objective learning

Review Process

Papers will be reviewed by at least two members of the Program Committee
Co-Chairs at EVANNAI, Computer Science Department, Universidad Carlos III de
Madrid

   - Ricardo Aler
   - Inés M. Galván
   - José M. Valls

Contact information

   - E-mail: aler at inf.uc3m.es, igalvan at inf.uc3m.es, jvalls at inf.uc3m.es
   - Postal Address: Avenida Universidad, 30; 28911 Leganés, Madrid (SPAIN)
   - Telephone: +34916249418
   - Fax: +34916249430
   - Url: http://eva.evannai.inf.uc3m.es/