Introduction to metaheuristics with Julia

This talk introduces the concept of metaheuristics, algorithms used to solve difficult optimization problems, by illustrating in parallel their implementation and use using julia, a high-level and high-performance programming language. Some approaches we will cover include the greedy heuristic, the greedy randomized search procedure, descent methods, variable neighborhood search, simulated annealing, tabu search and genetic algorithms.

Target audience : Master students, Bachelor students with interest in optimization, PhD training, anyone with some knowledge of programming but not particularly familiar with metaheuristics.

Recommended packages (open source): Julia 1.7.2, Anaconda3 / Jupyter

Data: martes 24 de maio de 2022
Lugar: Sala de Audiovisuais, Escola de Enxeñería Industrial, sede Campus
Hora: 16:00 h

Emitirase simultaneamente a través da sala REU da EEI-Campus:
Clave de alumno: cYW2XD5F
Campus Remoto directions

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