This page is under construction  forever…
I am interested in Artificial Intelligence, Operations Resarch and/or Computer Science projects.
See my Research interests and
Publications for more.
Please find below specific ideas for:
The difficulty level of a project can be adjusted
depending on the background of a student. Hence, the MSc students can have a look at the
third year project ideas, and similarly, third year students can have a look at the MSc project ideas while
deciding on a project topic.
I am open to the other project ideas, such as:
 intelligent search methods (particularly hyperheuristics and evolutionary algorithms) and their application to
challenging (preferably real world) problems,
 intelligent user interfaces
 visualisation tools
 intelligent agents for playing games (such as, backgammon, simino), and more.
You can also view the individual third year and group projects that I have offered previously at UoN for
inspiration. These links are provided in the menu on the left.
Additionally, here is a sample of the selected fourth year projects that I have supervised before:

Previously supervised fourth year projects [click here]
Third/Fourth Year Project Ideas
Please drop me an email, if you are interested interested in Artificial Intelligence,
Operations Research and/or Computer Science projects.

A meta/hyperheuristic for solving High School timetabling problem.
This project involves in implementing a solver
for high school timetabling and this will require joining a newly announced competition.
The details
can be reached from
ITC2011.

An intelligent PacMan player.
[See PACMAN competition]

A meta/hyperheuristic for solving a timetabling problem
(The timetabling problem and the meta/hyperheuristic to be used
as a solver can be decided later. See the competition tracks at
ITC2007
 you can pick one problem)
 Desing your own topic:
Proposals realted to the multiobjective search methods for solving multiobjective problems and
continuous optimisation methods, such as evolutionary strategies, particle swarm optimisation and differential
evolution are also welcome.
Please feel free to name X and Y for your self and drop me an email providing me your
proposal details.

A multimodal intelligent user interface for drawing trees.

A multimodal intelligent user interface for drawing graphs.

A visualisation tool for a Particle Swarm Optimisation System.

A visualisation tool for a hyperheuristic.

A tool for comparing bottom left heuristics for 2D strip packing

A hyperheuristic managing crossover

A tool for lanscape analysis of hyperheuristics (based on HyFlex  a java hyperheuristic library)
MSc Project Ideas
My project ideas provided below require implementation of intelligent search methodologies, such as, heuristics, metaheuristics or hyperheuristics
for solving
optimisation problems that belong to the NP hard set.
A thorough experimentation over a set of well known problem
instances and analysis of results are expected.
I have no specific programming language preference.
Please send me an email if you are a
masters student
on the MSc in

ACS (Advanced Computing Science)

IT (Information Technology)
 MIT (Management of Information Technology)
courses
and interested in Artificial Intelligence, Operations Research and/or Computer Science projects. You can combine
any intelligent search methodology for solving any type of problem provided below. Here are some
sample project topics:

Lanscape analysis of hyperheuristics

Hyperheuristics for unit commitment problem

Evaluation of representations in genetic algorithms for strip packing

Hyperheuristics for quadratic assignment problem

Memetic algorithms for quadratic assignment problem

Hyperheuristics
for strip packing

Hyperheuristics for quadratic assignment problem

Hyperheuristics for vehicle routing problem
A Sample of Intelligent Search Methodologies
Genetic/Memetic Algorithms
Genetic algorithms are nature inspired population based search methodologies for solving computationally difficult
problems. In this project, representations for ordering the rectangles
to be packed using genetic algorithms will be
investigated for strip packing over a set of benchmark problems.
A memetic algorithm hybridises a genetic algorithm with local search (hill climbing).
Hyperheuristics
A hyperheuristic is a heuristic search method that seeks to automate,
often by the incorporation of machine learning
techniques, the process of selecting, combining, generating or adapting several
simpler heuristics (or components of
such heuristics) to efficiently solve computational search problems.
There might be multiple heuristics from which one can choose for solving a problem, and each heuristic has its own strength and weakness. The idea is to automatically devise algorithms by combining the strength and compensating for the weakness of known heuristics.
In a typical selection hyperheuristic framework there is a highlevel methodology and a set of lowlevel heuristics (either constructive or perturbative heuristics). Given a problem instance, the highlevel method selects which lowlevel heuristic should be applied at any given time, depending upon the current problem state, or search stage.
A Sample of Problem Domains
Unit Commitment Problem
The methods for solving the Unit Commitment Problem requires a search for the best startup and shutdown schedules for a group of power generators,
minimising the power generation costs while
maintaining a forecasted amount of power during given periods.
Nurse Rostering Problem
A nurse roster is a timetable consisting of employee shift assignments and
the rest days of nurses in a healthcare institution. Some healthcare institutions
might be composed of several departments. A departmental roster is defined as a
collection of the nurse rosters of all nurses working within the same department.
Nurse Rostering Problems (NRPs) are timetabling problems that seek for satisfactory
schedules to be generated for the employees, employers and even for the
customers (patients).
Quadratic Assignment Problem
The quadratic assignment problem (QAP) is one of fundamental combinatorial optimization problems in the branch of optimization or operations research in mathematics, from the category of the facilities location problems.
The problem models the following reallife problem:
There are a set of n facilities and a set of n locations. For each pair of locations, a
distance is specified and for each pair of facilities a weight or flow is specified (e.g.,
the amount of supplies transported between the two facilities). The problem is to assign all facilities
to different locations with the goal of minimizing the sum of the distances multiplied by the corresponding
flows.
Strip Packing Problem
Cutting and packing a given stock material are crucial processes in manufacturing and producing. Paper, plastic, wood, glass, steel and leather are some of the most commonly used
stock materials in the industries. Different types of cutting and packing problems arise due to the nature of the stock material, such as different constraints and objectives. Orthogonal Rectangular Strip Packing Problem (SPP) involves in finding the best orthogonal placement of a set of
rectangles with given widths and heights in a suitable orientation on a strip of rectangular stock sheet with a fixed
width and an infinite height. Orthogonal placement requires the sides of the rectangles to be parallel to the edges
of the strip. Based on the recent categorization of the cutting and packing problems provided by Waescher, Haussner and Schumann (2007), SPP is identified as twodimensional, open dimension problem.
SPP is proven to be an NP hard problem.
Vehicle Routing Problem
The vehicle routing problem (VRP) is a combinatorial optimization and integer programming problem seeking to service a number of customers with a fleet of vehicles. Proposed by Dantzig and Ramser in 1959, VRP is an important problem in the fields of transportation, distribution and logistics. Often the context is that of delivering goods located at a central depot to customers who have placed orders for such goods. Implicit is the goal of minimizing the cost of distributing the goods. Many methods have been developed for searching for good solutions to the problem, but for all but the smallest problems, finding global minimum for the cost function is computationally complex. (wikipedia)
