HM 2016

10th International Workshop on Hybrid Metaheuristics

8-10 June 2016, Plymouth, United Kingdom

Please submit your papers via EasyChair website at:

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HM 2016 Special Sessions

Special sessions are organized as part of HM 2016 in order to encourage more interaction between several communities. Submission and publication rules are the same as the workshop.


Special session proposals are invited in all topics related with the aims and scopes of HM 2016. If you are interest, please send a proposal to hm2016 @, including title, aim and scope, list of main topics, and the names, contact details and short biography of the organizers.


Hybrid metaheuristics for Bioinformatics

Laetitia Jourdan, CRIStAL, INRIA, University Lille, France
Julia Handl, Decision and Congnitive Sciences Research Centre, University of Manchester, UK

Many applications in bioinformatics and computational biology give rise to large-scale optimization problems. These problems often present new challenges to existing optimization techniques, which derive from the scale (volume or dimensionality) or reliability of data (e.g. in optimization problems related to the analysis of omics data next-generation sequencing data), the complexity of the systems that are being modeled (e.g. in systems biology) and the size of the search space (e.g. in molecular docking applications). Hybrid meta-heuristics have the potential to make a crucial contribution in this setting, as they draw efficiency from the combination of the search ability of meta-heuristics and the strengths of other, domain-specific methods. This special session invites contributions related to method development and / or application of hybrid meta-heuristics for problems in computational biology and bioinformatics.


Areas of interest include (but are not restricted to):

  • Development of specialized hybrid meta-heuristics for applications in bioinformatics and computational biology
  • Novel theoretical and/or empirical insight regarding the performance of existing hybrid approaches on bioinformatics problems of interest
  • Applications of hybrid meta-heuristics in structural biology including protein structure prediction, protein docking, and protein-protein interaction
  • Applications related to network inference e.g. inference of gene regulatory networks or protein-protein interaction networks
  • Applications related to the analysis of different types of biological data including ‘omics data and next generation sequencing data


Contact details:,


Hybrid metaheuristics for Dynamic & Uncertain Environments

Amir Nakib, UPEC, Université Paris Est Créteil, France
Antonio D. Masegosa, Deusto Institute of Technology, University of Deusto, Spain
Mario Pavone, University of Catania, Italy

Many of the optimization problems we found in real world present some sort of dynamism or uncertainty in one or more of their components: objective function, dimension, constraints or variables’ domain. This type of problems has attracted the attention of the scientific community in recent years because their resolution entails big challenges for classic optimization methods. The combination of the strengths of different metaheuristics or the combination of metaheuristics and techniques from others domains (machine learning, classical artificial intelligence, fuzzy set theory, or probability theory) have shown to be one of the most successful tools to address these challenges. This special session aims at gathering the recent theoretical and practical advances, as well as exploring future directions in the field of hybrid metaheuristics for dynamic and uncertain environments.

The topics of the special session include both theoretical and experimental aspects of the application of hybrid metaheuristics to (but not restricted to):

  • Single and multi-objective dynamic optimization problems
  • Robust optimization
  • Noisy fitness functions
  • Approximate or imprecise fitness functions
  • Dynamic constrained optimization problems
  • Real-world problems with noise and/or dynamism
  • Surrogate-models for dynamic and uncertain environments
  • Theoretical results for problems with uncertainty and dynamism


Contact details:,,


Engineering Applications of Hybrid Metaheuristics

Alessandro Di Nuovo, Sheffield Hallam University, UK
Grazziela Figueredo, University of Nottingham, UK

Contact details:,


Hybrid metaheuristics in Operational Research

Djamila Ouelhadj, University of Portsmouth, UK
Grazziela Figueredo, University of Nottingham, UK
Mario Pavone, University of Catania, Italy

Contact details:,,


Metaheuristics for Medical Image Processing & Computer Vision

Roshan Joy Martis, Dept. of ECE, St. Joseph Engineering College, Karnataka, India
Steven Lawrence Fernandes, Dept. of ECE, Sahyadri College of Engineering & Management, Karnataka, India

Medical Image Processing and Computer Vision have gained much importance because it has the ability to learn, this makes it an important aspect for both cognitive psychology and artificial intelligence. Medical Image Processing and Computer Vision aims at designing algorithms that can learn from models and generate prediction on data, most methods are based on finding parameters that optimize a defined objective function. Then, efficient metaheuristics may allow enhancing the performance of such algorithms, it can be used at different levels, for instance, feature selection, training algorithm, classification algorithm, and clustering algorithm.

Areas of interest include (but are not restricted to):

  • Medical data classification and Medical Data complexity
  • Feature selection and Regression
  • Medical decision support and Medical Prognosis
  • Prediction and Clustering
  • Swarm intelligence and Classification
  • Model selection and Optimization
  • Face Recognition and Genetic Algorithms


Contact details:,