Keynote Speakers
Alexandre Madeira
CIDMA, Mathematics Department of University of Aveiro
BIO: Alexandre Madeira is an Associate Professor in the Department of Mathematics at the University of Aveiro and a member of the Center for Research and Development in Mathematics & Applications. His key contributions lie in parametric methods for constructing specialized logics tailored to specific applications. His groundbreaking work began during his PhD in 2013, where he developed a method to generate logics to design reconfigurable systems. Since then, his research has expanded to the generation of various non-classical logics, focusing on the specification and verification of programs with non-standard behaviours, including those involving weighted and paraconsistent computations.
TITLE: TBA
ABSTRACT: TBA.
Carlos M. Fonseca
CISUC, Department of Informatics Engineering
University of Coimbra
BIO: Carlos M. Fonseca is an Associate Professor at the Department of Informatics Engineering of the University of Coimbra, Portugal, and a member of the Adaptive Computation group of CISUC, the Centre for Informatics and Systems of the University of Coimbra. He obtained his doctoral degree from the University of Sheffield, U.K., where he also conducted post-doctoral research. His main research interests are in multiobjective optimisation, evolutionary computation, experimental evaluation of optimisation algorithms, and practical applications of optimisation. He currently focuses mainly on preference articulation in multiobjective optimisation and the computational modelling of optimisation problems. He serves as Action Chair and Grant Holder Scientific Representative of COST Action ROAR-NET, a European research and innovation network aimed at making randomised optimisation algorithms widely competitive in practice by identifying and reducing obstacles to their adoption at the scientific, technical, economic, and human levels.
TITLE: Metaheuristics for Combinatorial Optimisation: Models or Algorithms?
ABSTRACT: Evolutionary algorithms, ant colony optimisation, particle swarm optimisation, iterated local search, tabu search, and GRASP, among others, are examples of randomised optimisation algorithms that are often grouped together under the term metaheuristics and have shown success in solving many large combinatorial optimisation problems. Unfortunately, the flexibility with which they can be adapted to new problems has led to a situation where each such adaptation is presented as a new algorithm for a specific problem. An alternative view is that problem-specific features should be captured by, and encapsulated in, a model with which optimisation algorithms interact in a problem-independent way. This keynote presents ongoing work to make this vision a reality.