The huge computational cost of an exhaustive search for hard optimization problems is thwarted by the application of an exhaustive search, which is generally impossible to realize in a bearable time. For these reasons, heuristics and metaheuristics have been widely applied to tackle this type of problem. However, their application to specific problems requires problem-specific coding and parameter adjusting to produce good results. Hyperheuristics are new optimization approaches having a higher level of abstraction than metaheuristics. The strength of hyperheuristics is that they perform on a search space of low-level problem-specific heuristics rather than directly on the search space of solutions, as is the case with metaheuristic approaches. Hyperheuristics take advantage of machine learning techniques to decide when and where to apply every single low-level heuristic. Then, hyperheuristics could be easier to adapt to any specific optimization problem. Moreover, graph theory, machine learning, and social behavior could inspire the latter. Nowadays, this branch of artificial intelligence is called computational learning theory, which is the design and analysis of new algorithms to infer and/or discover and/or learn patterns to solve problems based on sample data. Nowadays the association of computational learning techniques with evolutionary computation is proved to be worth emphasizing. This special session focuses on, but is not limited to, new works showing original computational algorithms and proving their efficiency on well-known problems.
This special session is, mainly, expected to invite recent original research including but not limited to:
Computational learning with Genetic algorithms
Computational learning with Particle swarm
Computational learning with Honey bee optimization
Computational learning with Ant colony
Computational learning with Mimetic algorithms
Computational learning with Fireworks algorithms
Computational learning with cockroach algorithms
Evolutionary hyperheuristics including distributed and parallel ones
Evolutionary metaheuristics including distributed and parallel ones
Computational learning associated with evolutionary computation
Computational learning and optimization in bioinformatics
Applications for real-life problems
Please follow the IEEE CEC2023 paper submission. please make sure you select Computational learning theory and advances from Heuristics to Hyperheuristics: new trends and applications in hard optimization, and constraint reasoning in the Main Research topic. Special Session papers will be reviewed as other regular conference papers and those accepted will be included in the conference proceedings published by IEEE Explore.
This special session is supported by the IEEE CIS ECTC, Task Forces (Evolutionary Deep Learning and Applications, Evolutionary Computation for Feature Selection and Construction, Evolutionary Computer Vision and Image Processing) and IEEE ISATC.
Hajer BEN OTHMAN, Rochester Institute of Technology, Dubai, UAE
Sadok BOUAMAMA, ENSI, University of La Manouba, Tunisia & Higher Colleges of Technology, UAE
Maurice CLERC, Independent Consultant, France
Russell EBERHART, Purdue School of Engineering and Technology, Indianapolis, USA
Hamza GHARSELLAOUI, University of Carthage, Tunisia
David E. GOLDBERG, University of Illinois Urbana-Champaign, Illinois, USA
Moez HAMMAMI, ISG Tunis, SMART LAB, ISG Tunis, University of Tunis, Tunisia
James KENNEDY, US Bureau of Labor Statistics, Washington DC, USA
Ouajdi KORBAA, ISIT COM, University of Sousse, Tunisia
Moncef TAGINA, COSMOS, ENSI, University of Manouba, Tunisia
Elgazali TALBI, INRIA DOLPHIN, Polytech'Lille, University of Lille 1, France
Patrick SIARRY, University of Paris-Est Créteil, France
Dr. Hajer BEN OTHMAN, Rochester Institute of Technology, Dubai, UAE: hxbcad(at)rit.edu
Prof. Sadok BOUAMAMA, ENSI, University of Manouba, Tunisia & Higher Colleges of Technology, UAE: sadok.bouamama(at)ensi-uma.tn , sbouamama(at)hct.ac.ae
Dr. Moez HAMMAMI,SMART LAB, ISG Tunis, University of Tunis, Tunisia: moez.hammami(at)isg.rnu.tn
Prof. Patrick SIARRY, University of Paris-Est Créteil, France: siarry(at)u-pec.fr