Skip to main content

Research Repository

Advanced Search

Outputs (32)

Hyper-heuristics: learning to combine simple heuristics in bin-packing problems. (2002)
Presentation / Conference Contribution
Ross, P., Schulenburg, S., Marin-Blazquez, J. G., & Hart, E. (2002). Hyper-heuristics: learning to combine simple heuristics in bin-packing problems.

Evolutionary algorithms (EAs) often appear to be a ‘black box’, neither offering worst-case bounds nor any guarantee of optimality when used to solve individual problems. They can also take much longer than non-evolutionary methods. We try to addres... Read More about Hyper-heuristics: learning to combine simple heuristics in bin-packing problems..

Improving street based routing using building block mutations. (2002)
Presentation / Conference Contribution
Urquhart, N. B., Ross, P., Paechter, B., & Chisholm, K. (2002). Improving street based routing using building block mutations. In J. Gottlieb, E. Hart, & S. Cagnoni (Eds.), Applications of Evolutionary Computing: EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTI

Street based routing (SBR) is a real-world inspired routing problem that builds routes within an urban area for mail deliveries. The authors have previously attempted to solve this problem using an Evolutionary Algorithm (EA). In this paper the autho... Read More about Improving street based routing using building block mutations..

Clustering Moving Data with a Modified Immune Algorithm (2001)
Presentation / Conference Contribution
Hart, E., & Ross, P. (2001). Clustering Moving Data with a Modified Immune Algorithm. In E. Boers (Ed.), Applications of Evolutionary Computing (394-403). https://doi.org/10.1007/3-540-45365-2_41

In this paper we present a prototype of a new model for performing clustering in large, non-static databases. Although many machine learning algorithms for data clustering have been proposed, none appear to specifically address the task of clustering... Read More about Clustering Moving Data with a Modified Immune Algorithm.

Enhancing the performance of a GA through visualisation. (2000)
Presentation / Conference Contribution
Hart, E., & Ross, P. (2000). Enhancing the performance of a GA through visualisation. In Proceedings of GECCO-2000

This article describes a new tool for visualising genetic algorithms, (GAs) which is designed in order to allow the implicit mechanisms of the GA | i.e. crossover and mutation | to be thoroughly analysed. This allows the user to determine whether th... Read More about Enhancing the performance of a GA through visualisation..

Scheduling chicken catching - an investigation into the success of a genetic algorithm on a real world scheduling problem. (1999)
Journal Article
Hart, E., Ross, P., & Nelson, J. (1999). Scheduling chicken catching - an investigation into the success of a genetic algorithm on a real world scheduling problem. Annals of Operations Research, 92, 363-380. https://doi.org/10.1023/A%3A1018951218434

Genetic Algorithms (GAs) are a class of evolutionary algorithms that have been successfully applied to scheduling problems, in particular job-shop and flow-shop type problems where a number of theoretical benchmarks exist. This work applies a genet... Read More about Scheduling chicken catching - an investigation into the success of a genetic algorithm on a real world scheduling problem..

An immune system approach to scheduling in changing environments. (1999)
Presentation / Conference Contribution
Hart, E., & Ross, P. (1999). An immune system approach to scheduling in changing environments. In W. Banzhaf, J. M. Daida, A. E. Eiben, M. H. Garzon, V. Honavar, M. Jakiela, & R. E. Smith (Eds.), GECCO-99 : proceedings of the genetic and evolutionary comp

This paper describes the application of an artificial immune system, (AIS), model to a scheduling application, in which sudden changes in the scheduling environment require the rapid production of new schedules. The model operates in two phases: In t... Read More about An immune system approach to scheduling in changing environments..

A heuristic combination method for solving job-shop scheduling problems. (1998)
Presentation / Conference Contribution
Hart, E., & Ross, P. (1998). A heuristic combination method for solving job-shop scheduling problems. In A. E. Eiben, T. Back, M. Schoenauer, & H. Schwefel (Eds.), Parallel Problem Solving from Nature V (845-854). https://doi.org/10.1007/BFb0056926

This paper describes a heuristic combination based genetic algorithm, (GA), for tackling dynamic job-shop scheduling problems. Our approach is novel in that the genome encodes a choice of algorithm to be used to produce a set of schedulable operation... Read More about A heuristic combination method for solving job-shop scheduling problems..

Producing robust schedules via an artificial immune system. (1998)
Presentation / Conference Contribution
Hart, E., Ross, P., & Nelson, J. (1998). Producing robust schedules via an artificial immune system. In Proceedings of International Conference on Evolutionary Computing (464-469). https://doi.org/10.1109/ICEC.1998.699852

This paper describes an artificial immune system (AIS) approach to producing robust schedules for a dynamic jobshop scheduling problem in which jobs arrive continually, and the environment is subject to change due to practical reasons. We investi... Read More about Producing robust schedules via an artificial immune system..

An adaptive mutation scheme for a penalty-based graph-colouring GA. (1998)
Presentation / Conference Contribution
Ross, P., & Hart, E. (1998). An adaptive mutation scheme for a penalty-based graph-colouring GA. In A. E. Eiben, T. Back, M. Schoenauer, & H. Schwefel (Eds.), Parallel Problem Solving from Nature V (795-802). https://doi.org/10.1007/BFb0056921

The folklore of evolutionary algorithms still seems to contain some gross over-generalistions, such as that direct encodings are inferior to indirect ones, that penalty-function methods are often poor, and that observed performance on a few instances... Read More about An adaptive mutation scheme for a penalty-based graph-colouring GA..