Lecturer Overview

Alejandro Arbelaez received his PhD in computer science from Université Paris-Sud XI in 2011 for his work on applying learning-based techniques to solve combinatorial problems. He also holds an engineering degree from Universidad Javeriana (Cali, Colombia). After finishing his PhD he worked as a PostDoctoral Researcher at the University of Tokyo (2 years) and University College Cork (3.5 years). Dr. Arbelaez has over 40 publications, which have appeared in prestigious AI journals such as: Journal of Heuristics and Constraints, and he has presented his work at several conferences in the fields of artificial intelligence and computer science. His research interest include: Constraint Satisfaction Problems, Local Search, SAT, Network Design, Parallel Search, and Applying Machine Learning to solve hard combinatorial problems.

Short Description of module

Metaheuristic Optimisation explores techniques for the analysis and design of efficient techniques to solve real-life problems. In this module the learner will be introduced to the   complexity of solving hard combinatorial problems, i.e., recognise and prove NP-hard problems. Additionally, the module covers effective and efficient meta-heuristic techniques to tackle complex decision problems, especially combinatorial optimisation problems.

What to expect when taking the module

This module will teach the students the fundamental concepts on heuristics in solving various optimization problems with emphasis on meta-heuristics. The topics include basic heuristic methods; meta-heuristics such as simulated annealing, local search, iterative local search, genetic algorithms, ant colony optimization. Furthermore, we introduce the complexity classes P, NP, and NP-complete, and formulate the question P=NP?, and consider the implications of NP-completeness in the context general problem solving.

Public Recordings

The first two weeks of the module will be made public:

• Week 1
• Week 2

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