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Ender Özcan
Office: C86
T:+44(0) 115 95 15544
F:+44(0) 115 9514254

exo At cs-nott-ac-uk (replace all - with dot)

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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 hyper-heuristics 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.

  • Project coordination support tool (PESTO)
    This project involves in creating and interface and optimisation tools for maintanance and assignment of markers for Y3/Y4 dissertations as well as scheduling end of year project demonstrations without any clashes.
  • A metaheuristic for solving windfarm layout optimisation problem.
  • A hyper-heuristic for online bin packing
    The online bin packing problem is a well-known bin packing variant which requires immediate decisions to be made for the placement of a lengthy sequence of arriving items of various sizes one at a time into fixed capacity bins without any overflow. The overall goal is maximising the average bin fullness. Hyper-heuristics are high level search and optimisation methods which explore the space formed by low level heuristics or heuristic components for solving complex problems. This project involves implementation of selection hyper-heuristics and a set of low level heuristics based on a policy matrix representation for solving online bin packing problems. The project builds on a previous work which provides a Java framework. See the following paper for more:
  • A meta/hyper-heuristic 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. A meta/hyper-heuristic for solving a timetabling problem (The timetabling problem and the meta/hyper-heuristic to be used as a solver can be decided later. See the competition tracks at ITC2007 - you can pick one problem)
  • Any recent competition related to artificial intelligence can be used as a basis for a project. For example, ever year GECCO conference series organise mutliple competitions which would make a good project topic. [Click for the GECCO 2016 competitions]: These competitions are all very challenging and if chosen as a project topic, the results could potentially be published as a scientific paper.
  • An Intelligent Agent for Playing Game X. This project should involve implementation of AI techniques such as game trees, alpha beta pruning, Monte Carlo search, etc. AND a GUI relevant to the game X (please do name the game your self). I have previously supervised projects implementing intelligent players for Abalone, PacMan, Poker, Backgammon and more.
  • Desing your own topic:

    [Method X][Problem Y]

    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 hyper-heuristic.
  • A tool for comparing bottom left heuristics for 2D strip packing
  • A hyper-heuristic managing crossover
  • A tool for lanscape analysis of hyper-heuristics (based on HyFlex - a java hyper-heuristic library)

MSc Project Ideas

My project ideas provided below require implementation of intelligent search methodologies, such as, heuristics, meta-heuristics or hyper-heuristics 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 MSc student 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 hyper-heuristics
  • Hyper-heuristics for unit commitment problem
  • Evaluation of representations in genetic algorithms for strip packing
  • Hyper-heuristics for quadratic assignment problem
  • Memetic algorithms for quadratic assignment problem
  • Hyper-heuristics for strip packing
  • Hyper-heuristics for quadratic assignment problem
  • Hyper-heuristics 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).

A hyper-heuristic 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 hyper-heuristic framework there is a high-level methodology and a set of low-level heuristics (either constructive or perturbative heuristics). Given a problem instance, the high-level method selects which low-level 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 start-up and shut-down 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 health-care institution. Some health-care 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 real-life 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 two-dimensional, 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)