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Outputs (147)

A systematic investigation of GA performance on jobshop scheduling problems. (2003)
Presentation / Conference Contribution
Hart, E., & Ross, P. (2003). A systematic investigation of GA performance on jobshop scheduling problems. In Real-World Applications of Evolutionary Computing (280-289). https://doi.org/10.1007/3-540-45561-2_27

Although there has been a wealth of work reported in the literature on the application of genetic algorithms (GAs) to jobshop scheduling problems, much of it contains some gross over-generalisations, i.e that the observed performance of a GA on a sma... Read More about A systematic investigation of GA performance on jobshop scheduling problems..

Exploiting the analogy between immunology and sparse distributed memory. (2002)
Presentation / Conference Contribution
Hart, E., & Ross, P. (2002). Exploiting the analogy between immunology and sparse distributed memory. In J. Timmis, & P. J. Bentley (Eds.), ICARIS 2002 : 1st International Conference on Artificial Immune Systems (59-67)

The relationship between immunological memory and a class of associative memories known as sparse distributed memories (SDM) is well known. This paper proposes a new model for clustering non-stationary data based on a combination of salient features... Read More about Exploiting the analogy between immunology and sparse distributed memory..

Combining choices of heuristics. (2002)
Book Chapter
Ross, P., & Hart, E. (2002). Combining choices of heuristics. In R. Sarker, M. Mohammadian, & X. Yao (Eds.), Evolutionary Optimization (229-252). Kluwer

Multiple-goal learning in robots using the MAXSON neural network architecture. (2002)
Presentation / Conference Contribution
Webb, A., Ross, P., & Hart, E. (2002). Multiple-goal learning in robots using the MAXSON neural network architecture. In B. Hallam, D. Floreano, G. Hayes, J. A. Meyere, & J. Hallam (Eds.), SAB'02 Workshop: On Growing Up Artifacts that Live - Basic Princip

No abstract available.

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..

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.

Real-world applications of evolutionary computing (2000)
Presentation / Conference Contribution
Cagnoni, S., Poli, R., Smith, G. D., Corne, D., Oates, M., Hart, E., …Fogarty, T. C. (2000). Real-world applications of evolutionary computing. In Proceedings of EvoWorkshops 2000

This book constitutes the refereed proceedings of six workshops on evolutionary computation held concurrently as EvoWorkshops 2000 in Edinburgh, Scotland, UK, in April 2000. The 37 revised papers presented were carefully reviewed and selected by the... Read More about Real-world applications of evolutionary computing.

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..