Special Topic G

Description

Optimisation problems are everywhere, with applications in areas as logistics, transportation, telecommunication, production planning, health, molecular biology, bioinformatics, timetabling, machine learning and many others.

This course will start introducing fundamentals of combinatorial optimisation problems and their applications in different areas.

As we will see, most practical combinatorial optimisation problems are very complex and computational methods called metaheuristics can be very efficient. Evolutionary algorithms are a family of metaheuristics well known for finding good solutions for various kinds of complex problems.

The course introduces several metaheuristics as simulated annealing, tabu search with the main emphasis on evolutionary algorithms, genetic algorithms, memetic algorithms, differential evolution, among others.

Lectures will be focused on basic problem solving approaches using evolutionary algorithms which include problem formulation/modeling, encoding, operator selection, constraint handling etc. All of these aspects will be explained in view of real world problems.

Availability

Callaghan

  • Semester 2 - 2015
  • Semester 2 - 2016

Learning Outcomes

1. Understand in-depth knowledge in a specific area of ICT

2. Improve communication skills

3. Improve capacity to undertake postgraduate research

Content

This course will

- introduce fundamentals of combinatorial optimisation problems and their applications in different areas.

- introduce several metaheuristics as simulated annealing, tabu search with the main emphasis on evolutionary algorithms, genetic algorithms, memetic algorithms, differential evolution, among others.

Assumed Knowledge

Permission from Head of Discipline

Assessment Items

Project: Programming and Written Assignment

Presentation: Oral presentation

Contact Hours

Callaghan

Lecture

Face to Face On Campus 2 hour(s) per Week for Full Term