Special Sessions

Three special sessions are planned for the SEAL conference.

For submitting a paper to any of these special sessions, you should follow the normal paper submission process via EasyChair. In the Main Research Topic, please choose the relevant special session title.

Special session papers are treated the same as regular conference papers. All papers will be fully refereed by a minimum of two specialized referees. Before final acceptance, all referees comments must be considered. All accepted papers that are presented at the conference will be included in the conference proceedings, to be published in Lecture Notes in Computer Science (LNCS) by Springer. Selected papers will be invited for further revision and extension for possible publication in a special issue of two SCI journals after further review: Genetic Programming and Evolvable Machines (GPEM, springer, Impact Factor 1.333) and Soft Computing (Springer, Impact Factor 1.124).

Important Dates

14 July 2014 28 July 2014, extended and final deadline for submission of full papers (<=12 pages)
29 August 2014, Notification of acceptance
16 September 2014, Deadline for camera-ready copies of accepted papers
15-18 December 2014, Conference sessions (including tutorials and workshops)

Confirmed Special Sessions


Special Session 1: Evolutionary Feature Reduction

Large numbers of features/attributes are often problematic in machine learning and data mining. They lead to conditions known as "the curse of dimensionality". Feature reduction aims to solve this problem by selecting a small number of original features or constructing a smaller set of new features. Feature selection and construction are challenging tasks due to the large search space and feature interaction problems. Recently, there has been increasing interest in using evolutionary computation approaches to solve these problems.

The theme of this special session is the use of evolutionary computation for feature reduction, covering ALL different evolutionary computation paradigms including evolutionary algorithms, swarm intelligence, learning classifier systems, harmony search, artificial immune systems, and cross-fertilization of evolutionary computation and other techniques such as neural networks, and fuzzy and rough sets. This special session aims to investigate both the new theories and methods in different evolutionary computation paradigms to feature reduction, and the applications of evolutionary computation for feature reduction. Authors are invited to submit their original and unpublished work to this special session.

Topics of interest include but are not limited to:

Organizers:
Bing Xue, School of Engineering and Computer Science, Victoria University of Wellington
Kourosh Neshatian, Computer Science and Software Engineering, College of Engineering, University of Canterbury

Special Session 2: Evolutionary Machine Learning

Machine learning and evolutionary computation are two major fields of computational intelligence. They share many fundamental similarities and are frequently explored together to tackle complex, large-scale, and dynamic learning problems under various sources of uncertainties.

This special session will cover a broad range of topics related to evolutionary machine learning, including novel learning algorithms and their innovative applications. We will focus on both theoretical and practical research in this field. The aim is to show how the global search performed by evolutionary methods can complement the local search of non-evolutionary methods and how the combination of the two can improve learning effectiveness and performance within a wide range of clustering, classification, regression, prediction, and control tasks.

Topics of interest include, but not limited to:

Organizers:
Aaron Chen, School of Engineering and Computer Science, Victoria University of Wellington
Will Browne, School of Engineering and Computer Science, Victoria University of Wellington

Special Session 3: Evolutionary Scheduling and Combinatorial Optimisation

Evolutionary Scheduling and Combinatorial Optimization is an active research area in both Artificial Intelligence and Operations Research due to its applicability and interesting computational aspects. Evolutionary techniques are suitable for these problems since they are highly flexible in terms of handling constraints, dynamic changes and multiple conflicting objectives.

This special issue focuses on both theoretical and practical aspects of Evolutionary Scheduling and Combinatorial Optimization. Examples of evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimisation, particle swarm optimisation, evolutionary based hyper-heuristics, memetic algorithms.

Topics of interest include, but not limited to:

Organizers:
Su Nguyen, Victoria University of Wellington, New Zealand
Mengjie Zhang, Victoria University of Wellington, New Zealand
Kay Chen Tan, National University of Singapore, Singapore