Tuesday, 11 November 2014

Call for Papers CEC2015 Special Session "Evolutionary Algorithms in Hyper-Heuristics"

Hyper-heuristics aim to provide a generalized solution for a particular problem domain or across different problem domains. This is achieved by employing methods, such as metaheuristics, to combine or generate low-level heuristics. The low-level heuristics can be constructive, i.e. are used to create a solution, or perturbative, in which case the heuristics improve a candidate solution. Based on the function of the hyper-heuristic and type of low-level heuristics, a hyper-heuristic can be categorized as selection constructive, selection perturbative, generation constructive or generation perturbative. Evolutionary algorithms have been employed by hyper-heuristics and have played a pivotal role in the generation, hybridization and selection of low-level heuristics. Evolutionary algorithm hyper-heuristics have successfully been applied to various domains including timetabling, vehicle routing, decision tree induction, packing problems, text classification and dynamic environments amongst others. In certain domains, e.g. timetabling, selection perturbative hyper-heuristics have proven to be more effective than direct exploration of the solution space by evolutionary algorithms. Evolutionary algorithms, specifically genetic programming and grammatical evolution, have primarily been employed by hyper-heuristics to generate low-level heuristics. Recent trends in this field include the use of hyper-heuristics for algorithm design and hybridization of methods. Algorithm design essentially involves determining the parameter values and methods to use, e.g. the method of selection and crossover and mutation probabilities in ant algorithms. Hybridization is achieved by means of a selection perturbative hyper-heuristic to hybridize different approaches to solve the problem at hand, e.g. different multi-objective evolutionary algorithms are low-level heuristics in a selection perturbative hyper-heuristic to solve multi-objective optimization problems. The aim of this special session is for researchers to present recent developments in the field thereby paving the way for future advancement. The main topics include but are not limited to:
  • Applications of evolutionary algorithm hyper-heuristics
  • Theoretical aspects of evolutionary algorithm hyper-heuristics
  • Evolutionary algorithm hyper-heuristics for algorithm design
  • Evolutionary algorithm hyper-heuristics for the derivation of hybrid methods
  • Hybridization of evolutionary algorithm hyper-heuristics, i.e. the design of hyper-hyper-heuristics using evolutionary algorithms
  • Cross domain applications of evolutionary algorithm hyper-heuristics
  • Parallelization of evolutionary algorithm hyper-heuristics

Important Dates

Paper submission deadline: December 19, 2014
Paper acceptance notification: February 20, 2015
Final paper submission deadline: March 13, 2015
Early registration: March 13, 2015

Paper Submission

Special session papers are treated the same as regular papers and must be submitted via the CEC 2015 submission website. When submitting choose the "Evolutionary Algorithm in Hyper-Heuristics" special session from the "Main Research Topic" list.

Organizers

Nelishia Pillay, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, South Africa 
pillayn32@ukzn.ac.za

Nelishia Pillay is an associate professor in the School of Mathematics, Statistics and Computer Science at the University of KwaZulu-Natal. Her research areas include hyper-heuristics, combinatorial optimization, genetic programming, genetic algorithms and other biologically-inspired methods. She has published in these areas in journals, national and international conference proceedings. She is a member of the IEEE Task Force on Hyper-Heuristics with the Technical Committee of Intelligent Systems and Applications at IEEE Computational Intelligence Society and the Technical Committee on Soft Computing under the IEEE Systems, Man, and Cybernetics Society. She has served on program committees for numerous national and international conferences and is a reviewer for various international journals. She is an active researcher in field of evolutionary algorithm hyper-heuristics and the application thereof to combinatorial optimization problems. This is one of the focus areas of the NICOG(Nature-Inspired Computing Optimization) research group which she has established.

Rong Qu, School of Computer Science, University of Nottingham, United Kingdom 
Rong.Qu@nottingham.ac.uk

Dr Qu is an associate professor in the Automated Scheduling, Optimisation and Planning (ASAP) research group in the School of Computer Science in University of Nottingham. Her main research interests include meta-heuristic algorithms, knowledge based methodologies and constraint programming on modelling and solving complex optimisation and scheduling problems. She has published more than 40 refereed papers in international journals including Journal of Scheduling, Journal of Operational Research Society, European Journal of Operational Research, Journal of Heuristics and INFORMS Journal of Computing, etc. Dr Qu is a guest editor of a special issue on Artificial Intelligence Planning and Scheduling at the Journal of Scheduling. She is an invited speaker at the workshop of Self-tuning, self-configuring and self-generating search heuristics at the 11th International Conference on Parallel Problem Solving from Nature (PPSN 2010). She is the task force chair of Evolutionary Computation in Scheduling and Combinatorial Optimisation (2007-2021) and Hyper-heuristics (2013-) at IEEE CIS. Dr Qu is the program chair of five workshops or symposiums, and acted on the programme committee for more than 10 international conferences since 2010.

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