SPECIALSESSIONSTRANSFER LEARNING IN EVOLUTIONARYCOMPUTATION
Mengjie Zhang, Bing Xue, Harith Al-Sahaf and Yi Mei

Data mining, machine learning, and optimisation algorithms have achieved promises inmany real-world tasks, such as classification, clustering and regression. These algorithms canoften generalise well on data in the same domain, i.e. drawn from the same feature space
and with the same distribution. However, in many real-world applications, the available data
are often from different domains. For example, we may need to perform classification in one
target domain, but only have sufficient training data in another (source) domain, which may
be in a different feature space or follow a different data distribution. Transfer learning aims totransfer knowledge acquired in one problem domain, i.e. the source domain, onto anotherdomain, i.e. the target domain. Transfer learning has recently emerged as a new learning
framework and hot topic in data mining and machine learning.
Evolutionary computation techniques have been successfully applied to many real-world
problems, and started to be used to solve transfer learning tasks. Meanwhile, transfer learninghas attracted increasing attention from many disciplines, and has been used in evolutionary
computation to address complex and challenging issues. The theme of this special session istransfer learning in evolutionary computation, covering ALL different evolutionary
computation paradigms, including Genetic algorithms (GAs), Genetic programming (GP),
Evolutionary programming (EP), Evolution strategies (ES), Learning classifier systems (LCS),
Particle swarm optimization (PSO), Ant colony optimization (ACO), Differential evolution (DE),and Evolutionary Multi-objective optimization (EMO).
The aim is to investigate in both the new theories and methods on how transfer learning
can be achieved with different evolutionary computation paradigms, and how transfer
learning can be adopted in evolutionary computation, and the applications of evolutionary
computation and transfer learning in real-world problems.

Topics•

Evolutionary supervised transfer learning•

Evolutionary unsupervised transfer learning

Special Session Chair
Ke Tang · ketang@ustc.edu.chn

IEEE Conference on Evolutionary Computation
Donostia – San Sebastián, June 8-5
www.cec2017.org · info@cec2017.org

SPECIALSESSIONS•

Evolutionary semi-supervised transfer learning•

Domain adaptation and domain generalization in evolutionary computation•

Instance based transfer approaches in evolutionary computation•

Feature based transfer learning in evolutionary computation•

Parameter/model based transfer learning in evolutionary computation•

Relational based transfer learning in evolutionary computation•

Transfer learning in in evolutionary computation for classification•

Transfer learning in in evolutionary computation for regression•

Transfer learning in in evolutionary computation for clustering•

Transfer learning in in evolutionary computation for other data mining tasks, such as
association rules and link analysis•

Transfer learning in in evolutionary computation for scheduling and combinatorial
optimisation tasks•

Hybridisation of evolutionary computation and neural networks, and fuzzy systems
for transfer learning•

Hybridisation of evolutionary computation and information theory, statistics, etc., fortransfer learning machine learning•

Transfer learning in evolutionary computation for real-world applications, e.g. text
mining, image analysis, face recognition, WiFi localisation, etc.

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