1 code implementation • 11 Apr 2023 • Mehedi Hasan, Moloud Abdar, Abbas Khosravi, Uwe Aickelin, Pietro Lio', Ibrahim Hossain, Ashikur Rahman, Saeid Nahavandi
In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks.
no code implementations • 26 Mar 2023 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
However, such a learning rate is typically considered to be ``slow", compared to a ``fast rate" of $O(\lambda/n)$ in many learning scenarios.
no code implementations • 26 Nov 2022 • Yuan Sun, Su Nguyen, Dhananjay Thiruvady, XiaoDong Li, Andreas T. Ernst, Uwe Aickelin
Finally, we demonstrate that hybridising the machine learning-based variable ordering methods with traditional domain-based methods is beneficial.
no code implementations • 26 Nov 2022 • Yuan Sun, Winton Nathan-Roberts, Tien Dung Pham, Ellen Otte, Uwe Aickelin
In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task.
no code implementations • 12 Jul 2022 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Specifically, we provide generalization error upper bounds for the empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
no code implementations • 10 May 2022 • Xuetong Wu, Mingming Gong, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
We show that in causal learning, the excess risk depends on the size of the source sample at a rate of O(1/m) only if the labelling distribution between the source and target domains remains unchanged.
no code implementations • 6 May 2022 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
However, such a learning rate is typically considered to be "slow", compared to a "fast rate" of O(1/n) in many learning scenarios.
no code implementations • 26 Jan 2022 • Zahra Ghasemi, Hadi Akbarzadeh Khorshidi, Uwe Aickelin
This study concentrates on clustering problems and aims to find compact clusters that are informative regarding the outcome variable.
no code implementations • 3 Sep 2021 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Transfer learning is a machine learning paradigm where knowledge from one problem is utilized to solve a new but related problem.
no code implementations • 4 May 2021 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem.
no code implementations • 17 Dec 2020 • Goce Ristanoski, Jon Emery, Javiera Martinez-Gutierrez, Damien Mccarthy, Uwe Aickelin
As past medical data about a patient can be incomplete, irregular or missing, this creates additional challenges when attempting to use the patient's history for any new diagnosis.
no code implementations • 15 Dec 2020 • Yuxuan, Yang, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Aditi Nevgi, Elif Ekinci
This has resulted in the creation of numerous risk stratification tools with the objective of formulating associated surgical risk to assist both surgeons and patients in decision-making.
no code implementations • 15 Dec 2020 • Mansoureh Maadia, Uwe Aickelin, Hadi Akbarzadeh Khorshidi
In this paper, in addition to implementing a new aggregation approach in ensemble learning, we designed some experiments to encourage researchers to use interval modeling in ensemble learning because it preserves more uncertainty and this leads to more accurate classification.
no code implementations • 4 Dec 2020 • Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin
The similarity measure proposed within this study contains several features and attributes, of which are novel to aggregated fuzzy numbers.
no code implementations • 4 Dec 2020 • Hadi A. Khorshidi, Michael Kirley, Uwe Aickelin
We investigate the reliability and robustness of the proposed model using experiments by defining several scenarios in dealing with missing values and classification.
no code implementations • 4 Dec 2020 • J Liu, R Bai, Z Lu, P Ge, D Liu, Uwe Aickelin
This study proposes a novel regular expression-based text classification method making use of genetic programming (GP) approaches to evolve regular expressions that can classify a given medical text inquiry with satisfactory precision and recall while allow human to read the classifier and fine-tune accordingly if necessary.
no code implementations • 3 Dec 2020 • Ning Xue, Ruibin Bai, Rong Qu, Uwe Aickelin
This paper extends the previous work and presents a significantly more efficient approach by hybridising pricing and cutting strategies with metaheuristics (a variable neighbourhood search and a genetic algorithm).
no code implementations • 3 Dec 2020 • Justin Kane Gunn, Hadi Akbarzadeh Khorshidi, Uwe Aickelin
The two proposed ranking methods within this study contain the combination and application of previously proposed similarity measures, along with attributes novel to that of aggregated fuzzy numbers from interval-valued data.
no code implementations • 1 Dec 2020 • Hadi A. Khorshidi, Uwe Aickelin
The proposed MCGDM algorithm aggregates the data, determines the optimal weights for criteria and ranks alternatives with no further input.
no code implementations • 1 Dec 2020 • Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Zobaida Edib, Michelle Peate
Also, missing values are unavoidable in health and medical datasets and tackling the problem arising from the inadequate instances and missingness is not straightforward (Snell, et al. 2017, Sterne, et al. 2009).
no code implementations • 16 Nov 2020 • Chris Roadknight, Prapa Rattadilok, Uwe Aickelin
Much of the teaching of machine learning focuses on iterative hill-climbing approaches and the use of local knowledge to gain information leading to local or global maxima.
no code implementations • 16 Nov 2020 • Chaofan Tu, Ruibin Bai, Zheng Lu, Uwe Aickelin, Peiming Ge, Jianshuang Zhao
In this paper, we propose a rule-based engine composed of high quality and interpretable regular expressions for medical text classification.
no code implementations • 16 Nov 2020 • J Navrro, C Wagner, Uwe Aickelin, L Green, R Ashford
The measure has been specifically designed for assessing this agreement in fuzzy sets which are generated from data such as patient responses.
no code implementations • 16 Nov 2020 • Xiang Li, Xinyu Fu, Zheng Lu, Ruibin Bai, Uwe Aickelin, Peiming Ge, Gong Liu
Internet hospital is a rising business thanks to recent advances in mobile web technology and high demand of health care services.
no code implementations • 16 Nov 2020 • B Farhadinia, Uwe Aickelin, HA Khorshidi
This verifies that we have to implement fuzzy measures for modelling the interaction phenomena among the criteria. On the other hand, based on the recent extension of hesitant fuzzy set, called higher order hesitant fuzzy set (HOHFS) which allows the membership of a given element to be defined in forms of several possible generalized types of fuzzy set, we encourage to propose the higher order hesitant fuzzy (HOHF) Choquet integral operator.
no code implementations • 16 Nov 2020 • Bahram Farhadinia, Uwe Aickelin, Hadi Akbarzadeh Khorshidi
This contribution reviews critically the existing entropy measures for probabilistic hesitant fuzzy sets (PHFSs), and demonstrates that these entropy measures fail to effectively distinguish a variety of different PHFSs in some cases.
no code implementations • 16 Nov 2020 • Xuetong Wu, Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Zobaida Edib, Michelle Peate
Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years.
no code implementations • 16 Nov 2020 • Xiaoping Jiang, Ruibin Bai, Dario Landa-Silva, Uwe Aickelin
Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP).
no code implementations • 16 Nov 2020 • Uwe Aickelin, Jenna Marie Reps, Peer-Olaf Siebers, Peng Li
In this paper, we present a case study demonstrating how dynamic and uncertain criteria can be incorporated into a multicriteria analysis with the help of discrete event simulation.
no code implementations • 16 Nov 2020 • Hadi Akbarzadeh Khorshidi, Uwe Aickelin, Gholamreza Haffari, Behrooz Hassani-Mahmooei
The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury.
2 code implementations • 9 Nov 2020 • Hadi A. Khorshidi, Uwe Aickelin
Moreover, existing methods that generate synthetic instances using the majority class distributional information cannot perform effectively when the majority class has a multi-modal distribution.
no code implementations • 18 May 2020 • Xuetong Wu, Jonathan H. Manton, Uwe Aickelin, Jingge Zhu
Specifically, we provide generalization error upper bounds for general transfer learning algorithms and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase.
1 code implementation • 4 Jun 2017 • Xinyu Fu, Eugene Ch'ng, Uwe Aickelin, Simon See
We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM.
no code implementations • 9 Sep 2016 • Polla Fattah, Uwe Aickelin, Christian Wagner
External clustering indices were originally used to measure the difference between suggested clusters in terms of clustering algorithms and ground truth labels for items provided by experts.
no code implementations • 30 Aug 2016 • Simon Miller, Christian Wagner, Uwe Aickelin, Jonathan M. Garibaldi
An important role carried out by cyber-security experts is the assessment of proposed computer systems, during their design stage.
no code implementations • 5 Aug 2016 • Jan Feyereisl, Uwe Aickelin
The field of computer security tends to accept the latter view as a more appropriate approach due to its more workable validation and verification possibilities.
no code implementations • 21 Jul 2016 • Javier Navarro, Christian Wagner, Uwe Aickelin, Lynsey Green, Robert Ashford
As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals.
no code implementations • 21 Jul 2016 • Javier Navarro, Christian Wagner, Uwe Aickelin
Fuzzy Rule-Based Classification Systems (FRBCSs) have the potential to provide so-called interpretable classifiers, i. e. classifiers which can be introspective, understood, validated and augmented by human experts by relying on fuzzy-set based rules.
no code implementations • 21 Jul 2016 • Jenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin, Jack E. Gibson, Richard B. Hubbard
Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality.
no code implementations • 21 Jul 2016 • Christopher Roadknight, Durga Suryanarayanan, Uwe Aickelin, John Scholefield, Lindy Durrant
The relationship between severity of tumour, based on TNM staging, and survival is still unclear for patients with TNM stage 2 and 3 tumours.
no code implementations • 20 Jul 2016 • Grazziela P. Figueredo, Peer-Olaf Siebers, Uwe Aickelin, Amanda Whitbrook, Jonathan M. Garibaldi
Immunosenescence, the ageing of the immune system, is highly correlated to the negative effects of ageing, such as the increase of auto-inflammatory diseases and decrease in responsiveness to new diseases.
no code implementations • 20 Jul 2016 • Jenna Reps, Zhaoyang Guo, Haoyue Zhu, Uwe Aickelin
Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner.
no code implementations • 20 Jul 2016 • Tao Zhang, Peer-Olaf Siebers, Uwe Aickelin
In this paper we investigate user learning in authoritative technology adoption by developing an agent-based model using the case of council-led smart meter deployment in the UK City of Leeds.
no code implementations • 20 Jul 2016 • Jiangang Ma, Le Sun, Hua Wang, Yanchun Zhang, Uwe Aickelin
Uncertain data streams have been widely generated in many Web applications.
no code implementations • 20 Jul 2016 • Polla Fattah, Uwe Aickelin, Christian Wagner
Based on these initial classifications, the optimisation process tries to find an improved classifier which produces the best possible compact classes of objects (players) for every time point in the temporal data.
no code implementations • 20 Jul 2016 • Rodrigo Scarpel, Alexandros Ladas, Uwe Aickelin
In order to build such risk profiles it is necessary to employ an approach that analyses data systematically in order to detect important relationships, interactions, dependencies and associations amongst the available continuous and categorical variables altogether and accurately generate profiles of most interesting household segments according to their credit risk.
no code implementations • 20 Jul 2016 • Tao Zhang, Peer-Olaf Siebers, Uwe Aickelin
In this paper, we develop an agent-based model which integrates four important elements, i. e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, based on a case study, to simulate the energy consumption in office buildings.
no code implementations • 20 Jul 2016 • Jenna M. Reps, Uwe Aickelin, Richard B. Hubbard
We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms.
no code implementations • 30 Apr 2016 • Xinyu Fu, Eugene Ch'ng, Uwe Aickelin, Lanyun Zhang
Novelty detection in news events has long been a difficult problem.
no code implementations • 24 Feb 2015 • Jenna Reps, Uwe Aickelin, Jonathan Garibaldi, Chris Damski
The results showed there were clear differences in the way the profiles interact with the different advert genres and the results of this paper suggest that mobile advert targeting would improve the frequency that users interact with an advert.
no code implementations • 20 Feb 2015 • Alexandros Ladas, Eamonn Ferguson, Uwe Aickelin, Jon Garibaldi
Modelling Consumer Indebtedness has proven to be a problem of complex nature.
no code implementations • 20 Feb 2015 • Jenna M. Reps, Uwe Aickelin, Jiangang Ma, Yanchun Zhang
Side effects of prescribed medications are a common occurrence.
no code implementations • 3 Sep 2014 • Alexandros Ladas, Jonathan M. Garibaldi, Rodrigo Scarpel, Uwe Aickelin
Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models.
no code implementations • 3 Sep 2014 • Josie McCulloch, Christian Wagner, Uwe Aickelin
This is especially true where a large number of fuzzy sets are being compared as part of a reasoning system.
no code implementations • 3 Sep 2014 • Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
In this paper we investigate potential attributes that can be used in causal inference to identify side effects based on the Bradford-Hill causality criteria.
no code implementations • 3 Sep 2014 • Ian Dent, Tony Craig, Uwe Aickelin, Tom Rodden
Whether using the variability of regular behaviour allows the creation of more consistent groupings of households is investigated and compared with daily load profile clustering.
no code implementations • 3 Sep 2014 • Josie C. McCullochy, Chris J. Hinde, Christian Wagner, Uwe Aickelin
The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory, however, distance measures currently within the literature use a crisp value to represent the distance between fuzzy sets.
no code implementations • 3 Sep 2014 • Jenna M. Reps, Uwe Aickelin, Jonathan M. Garibaldi
The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier.
no code implementations • 2 Sep 2014 • Qi Chen, Amanda Whitbrook, Uwe Aickelin, Chris Roadknight
In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems.
no code implementations • 2 Sep 2014 • Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack Gibson, Richard Hubbard
In this paper we apply four existing electronic healthcare database signal detecting algorithms (MUTARA, HUNT, Temporal Pattern Discovery and modified ROR) on the THIN database for a selection of drugs from six chosen drug families.
no code implementations • 2 Sep 2014 • Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of undesirable side effects.
no code implementations • 2 Sep 2014 • Yihui Liu, Uwe Aickelin
Then feature selection methods are performed on feature matrix to detect the significant features.
no code implementations • 2 Sep 2014 • Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
no code implementations • 2 Sep 2014 • Chris Roadknight, Uwe Aickelin, John Scholefield, Lindy Durrant
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.
no code implementations • 23 Aug 2013 • Josie McCulloch, Christian Wagner, Uwe Aickelin
The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory.
no code implementations • 23 Aug 2013 • Josie McCulloch, Christian Wagner, Uwe Aickelin
Similarity measures provide one of the core tools that enable reasoning about fuzzy sets.
no code implementations • 23 Aug 2013 • Uwe Aickelin, Dipankar Dasgupta, Feng Gu
The innate immune system is an unchanging mechanism that detects and destroys certain invading organisms, whilst the adaptive immune system responds to previously unknown foreign cells and builds a response to them that can remain in the body over a long period of time.
no code implementations • 23 Aug 2013 • Naisan Benatar, Uwe Aickelin, Jonathan M. Garibald
Performance measurement of robotic controllers based on fuzzy logic, operating under uncertainty, is a subject area which has been somewhat ignored in the current literature.
no code implementations • 8 Jul 2013 • Ian Dent, Tony Craig, Uwe Aickelin, Tom Rodden
Changes in the UK electricity market, particularly with the roll out of smart meters, will provide greatly increased opportunities for initiatives intended to change households' electricity usage patterns for the benefit of the overall system.
no code implementations • 8 Jul 2013 • Alexandros Ladas, Uwe Aickelin, Jon Garibaldi, Eamonn Ferguson
It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order to discover more comprehensive knowledge regarding complicated economic behaviours.
no code implementations • 5 Jul 2013 • Chris Roadknight, Uwe Aickelin, Alex Ladas, Daniele Soria, John Scholefield, Lindy Durrant
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours.
no code implementations • 5 Jul 2013 • Chris Roadknight, Uwe Aickelin, Guoping Qiu, John Scholefield, Lindy Durrant
For predicting the stage of cancer from the immunological attributes, anti-learning approaches outperform a range of popular algorithms.
no code implementations • 5 Jul 2013 • Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects.
no code implementations • 4 Jul 2013 • Ian Dent, Christian Wagner, Uwe Aickelin, Tom Rodden
Changes in the UK electricity market mean that domestic users will be required to modify their usage behaviour in order that supplies can be maintained.
no code implementations • 4 Jul 2013 • Ian Dent, Uwe Aickelin, Tom Rodden
This paper describes a method for defining representative load profiles for domestic electricity users in the UK.
no code implementations • 4 Jul 2013 • Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses.
no code implementations • 4 Jul 2013 • Feng Gu, Jan Feyereisl, Robert Oates, Jenna Reps, Julie Greensmith, Uwe Aickelin
It is found that this feature, while advantageous for noisy, time-ordered classification, is not as useful as a traditional static filter for processing a synthetic dataset.
no code implementations • 4 Jul 2013 • Yihui Liu, Uwe Aickelin
Adverse drug reaction (ADR) is widely concerned for public health issue.
no code implementations • 4 Jul 2013 • Hala Helmi, Jon M. Garibaldi, Uwe Aickelin
Therefore, in this paper, we suggest Transductive Support Vector Machines (TSVMs) as semi-supervised learning algorithms, learning with both labelled samples data and unlabelled samples to perform the classification of microarray data.
no code implementations • 4 Jul 2013 • Naisan Benatar, Uwe Aickelin, Jonathan M. Garibaldi
It has been suggested that, when faced with large amounts of uncertainty in situations of automated control, type-2 fuzzy logic based controllers will out-perform the simpler type-1 varieties due to the latter lacking the flexibility to adapt accordingly.
no code implementations • 3 Jul 2013 • Naisan Benatar, Uwe Aickelin, Jonathan M. Garibaldi
Type-1 fuzzy logic has frequently been used in control systems.
no code implementations • 3 Jul 2013 • Jenna Reps, Jan Feyereisl, Jonathan M. Garibaldi, Uwe Aickelin, Jack E. Gibson, Richard B. Hubbard
In this paper, existing methods developed for spontaneous reporting databases are implemented on both a spontaneous reporting database and a general practice electronic health-care database and compared.
no code implementations • 3 Jul 2013 • Ian Dent, Uwe Aickelin, Tom Rodden
This paper takes an approach to clustering domestic electricity load profiles that has been successfully used with data from Portugal and applies it to UK data.
no code implementations • 31 May 2013 • Jan Feyereisl, Uwe Aickelin
This method has the ability to utilize a wide variety of clustering techniques, individually or in combination, while fusing privileged and technical data for improved clustering.
no code implementations • 31 May 2013 • Yihui Liu, Uwe Aickelin, Jan Feyereisl, Lindy G. Durrant
How to select the significant biomarkers from hundreds of protein markers is a key step in survival analysis.
no code implementations • 31 May 2013 • Feng Gu, Julie Greensmith, Uwe Aickelin
Our analysis suggests that the standard dDCA has a runtime complexity of O(n2) for the worst-case scenario, where n is the number of input data instances.
no code implementations • 31 May 2013 • Tao Zhang, Peer-Olaf Siebers, Uwe Aickelin
In this paper, we develop an agent-based model which integrates four important elements, i. e. organisational energy management policies/regulations, energy management technologies, electric appliances and equipment, and human behaviour, to simulate the electricity consumption in office buildings.
no code implementations • 31 May 2013 • Grazziela P. Figueredo, Peer-Olaf Siebers, Uwe Aickelin
Agent-based modelling and simulation is an alternative paradigm to ODE models that overcomes these limitations.
no code implementations • 31 May 2013 • Amanda Whitbrook, Uwe Aickelin, Jonathan M. Garibaldi
In mobile robotics, a solid test for adaptation is the ability of a control system to function not only in a diverse number of physical environments, but also on a number of different robotic platforms.
no code implementations • 31 May 2013 • Chris Roadknight, Uwe Aickelin, Galina Sherman
Modelling and simulating the traffic of heavily used but secure environments such as seaports and airports is of increasing importance.
no code implementations • 31 May 2013 • Feng Gu, Julie Greensmith, Uwe Aickelin
Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.
no code implementations • 31 May 2013 • William Wilson, Phil Birkin, Uwe Aickelin
In this paper we test the flexibility of the motif tracking algorithm by applying it to the search for patterns in two industrial data sets.
no code implementations • 30 May 2013 • Uwe Aickelin, Julie Greensmith, Jamie Twycross
The use of artificial immune systems in intrusion detection is an appealing concept for two reasons.
no code implementations • 30 May 2013 • Peer-Olaf Siebers, Uwe Aickelin, David Menachof, Galina Sherman, Peter Zimmerman
The efficiency of current cargo screening processes at sea and air ports is unknown as no benchmarks exists against which they could be measured.
no code implementations • 30 May 2013 • William Wilson, Uwe Aickelin
Memory can be defined as the ability to retain and recall information in a diverse range of forms.
no code implementations • 30 May 2013 • Uwe Aickelin
This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.
no code implementations • 20 Mar 2008 • Uwe Aickelin, Paul White
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms.