Aim and Scope
Since the notion of a fitness landscape was introduced by Sewell Wright in 1932, fitness land- scapes have been studied by evolutionary biologists to better understand how evolution occurs in nature. In a similar way, researchers in evolutionary computation (EC) have used fitness landscapes to better understand the evolutionary process of search. Studies have ranged from theoretical models of fully enumerated combinatorial landscapes to the prediction of algorithm performance based on approximate fitness landscape characteristics. Fitness landscape analy- sis is a growing field in the EC community, but research has been scattered in widely different publications and conferences. Research papers in fitness landscapes are often incorporated into theoretical tracks of conferences, even when the research is focused on the practical application of fitness landscape analysis. Alternatively, papers on fitness landscapes may appear alone in specific algorithm tracks, such as swarm intelligence or genetic programming.
The aim of this special session on fitness landscapes is to provide an opportunity to not only bring fitness landscape analysis researchers together at WCCI 2020, but also to publish the most recent work in a dedicated track in the proceedings. In addition, the special session should be of interest to researchers and practitioners interested in practically applying fitness landscape analysis techniques to better understand problems and algorithm behaviour.
For this special session on fitness landscapes we invite researchers to submit unpublished work specifically focusing on the practice of fitness landscape analysis. Topics of interest include, but are not limited to:
- Analysis of algorithm performance in relation to fitness landscape characteristics.
- Practical techniques for characterising the features of combinatorial problems with large
- search spaces or approximating the features of continuous search spaces.
- Practical measures for characterising dynamic landscapes.
- Practical analysis of the fitness landscapes of constrained optimisation problems.
- Characterisation of multiobjective optimisation problems.
- Online fitness landscape analysis for the characterisation of problems during search.
- Analysis of benchmark problem suites using fitness landscape techniques.
- Generation of new benchmark problems with particular fitness landscape characteristics.
- Analysis of the fitness landscapes of specific classes of problems or real-world optimisation problem instances to provide insight into algorithm behaviour or to highlight challenges in the practical application of fitness landscape analysis.
- Dr. Edgar Galvan, Department of Computer Science, Maynooth University. Email: email@example.com
- Prof. Katherine M. Malan, Department of Decision Sciences, University of South Africa. Email: firstname.lastname@example.org
- Dr. Nadarajen Veerapen, Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL), University of Lille. Email: email@example.com
- Prof. Gabriela Ochoa, School of Natural Sciences, University of Stirling, Email: firstname.lastname@example.org
- Edgar Galvan, Maynooth University.
- Katherine M. Malan, University of South Africa.
- Nadarajen Veerapen, University of Lille.
- Gabriela Ochoa, University of Stirling.
- James McDermott, Galway University.
- Leonardo Trujillo, Instituto Tecnologico de Tijuana.
- Sarah Thomson, University of Stirling.
- Efren Mezura. University of Veracruz.
- Andres Munoz Acosta. Melborne University.
- Sebastien Verel. Université du Littoral Côte d'Opale (ULCO), France.