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Author = O'Neill, Michael;
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Displaying Results 1 - 25 of 88 on page 1 of 4
Marked
Mark
A comparative study of multi-objective machine reassignment algorithms for data centres
(2019)
Saber, Takfarinas; Gandibleux, Xavier; O'Neill, Michael; Murphy, Liam, B.E.; Ventr...
A comparative study of multi-objective machine reassignment algorithms for data centres
(2019)
Saber, Takfarinas; Gandibleux, Xavier; O'Neill, Michael; Murphy, Liam, B.E.; Ventresque, Anthony
Abstract:
At a high level, data centres are large IT facilities hosting physical machines (servers) that often run a large number of virtual machines (VMs)— but at a lower level, data centres are an intricate collection of interconnected and virtualised computers, connected services, complex service-level agreements. While data centre managers know that reassigning VMs to the servers that would best serve them and also minimise some cost for the company can potentially save a lot of money—the search space is large and constrained, and the decision complicated as they involve different dimensions. This paper consists of a comparative study of heuristics and exact algorithms for the Multi-objective Machine Reassignment problem. Given the common intuition that the problem is too complicated for exact resolutions, all previous works have focused on various (meta)heuristics such as First-Fit, GRASP, NSGA-II or PLS. In this paper, we show that the state-of-art solution to the single objective formu...
http://hdl.handle.net/10197/11192
Marked
Mark
A comparison of GE and TAGE in dynamic environments
(2012)
Murphy, Eoin; O'Neill, Michael; Brabazon, Anthony
A comparison of GE and TAGE in dynamic environments
(2012)
Murphy, Eoin; O'Neill, Michael; Brabazon, Anthony
Abstract:
Paper presented at the ACM Genetic and Evolutionary Computation Conference, GECCO 2011, 12-16 July, Dublin, Ireland
The lack of study of genetic programming in dynamic environments is recognised as a known issue in the field of genetic programming. This study compares the performance of two forms of genetic programming, grammatical evolution and a variation of grammatical evolution which uses tree-adjunct grammars, on a series of dynamic problems. Mean best fitness plots for the two representations are analysed and compared.
Science Foundation Ireland
ti, ke, ab, co, li - TS 02.12
http://hdl.handle.net/10197/3516
Marked
Mark
A Hybrid Algorithm for Multi-objective Test Case Selection
(2019)
Saber, Takfarinas; Delavernhe, Florian; Papdakis, Mike; O'Neill, Michael; Ventresq...
A Hybrid Algorithm for Multi-objective Test Case Selection
(2019)
Saber, Takfarinas; Delavernhe, Florian; Papdakis, Mike; O'Neill, Michael; Ventresque, Anthony
Abstract:
IEEE Congress on Evolutionary Computation (CEC) 2018, Rio de Janerio, Brazil, 8-13 July 2018
Testing is crucial to ensure the quality of software systems – but testing is an expensive process, so test managers try to minimise the set of tests to run to save computing resources and speed up the testing process and analysis. One problem is that there are different perspectives on what is a good test and it is usually not possible to compare these dimensions. This is a perfect example of a multi-objective optimisation problem, which is hard — especially given the scale of the search space here. In this paper, we propose a novel hybrid algorithm to address this problem. Our method is composed of three steps: a greedy algorithm to find quickly some good solutions, a genetic algorithm to increase the search space covered and a local search algorithm to refine the solutions. We demonstrate through a large scale empirical evaluation that our method is more reliable (better whatever the ...
http://hdl.handle.net/10197/9985
Marked
Mark
A Hybrid Algorithm for Multi-objective Test Case Selection
(2019)
Saber, Takfarinas; Delavernhe, Florian; Papadakis, Mike; O'Neill, Michael; Ventres...
A Hybrid Algorithm for Multi-objective Test Case Selection
(2019)
Saber, Takfarinas; Delavernhe, Florian; Papadakis, Mike; O'Neill, Michael; Ventresque, Anthony
Abstract:
The 2018 IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, 8-13 July 2018
Testing is crucial to ensure the quality of software systems-but testing is an expensive process, so test managers try to minimise the set of tests to run to save computing resources and speed up the testing process and analysis. One problem is that there are different perspectives on what is a good test and it is usually not possible to compare these dimensions. This is a perfect example of a multi-objective optimisation problem, which is hard-especially given the scale of the search space here. In this paper, we propose a novel hybrid algorithm to address this problem. Our method is composed of three steps: a greedy algorithm to find quickly some good solutions, a genetic algorithm to increase the search space covered and a local search algorithm to refine the solutions. We demonstrate through a large scale empirical evaluation that our method is more reliable (better whatever the...
http://hdl.handle.net/10197/10479
Marked
Mark
A non-destructive grammar modification approach to modularity in grammatical evolution
(2012)
Swafford, John Mark; Hemberg, Erik; O'Neill, Michael; Nicolau, Miguel; Brabazon, A...
A non-destructive grammar modification approach to modularity in grammatical evolution
(2012)
Swafford, John Mark; Hemberg, Erik; O'Neill, Michael; Nicolau, Miguel; Brabazon, Anthony
Abstract:
Presented at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011
Modularity has proven to be an important aspect of evolutionary computation. This work is concerned with discovering and using modules in one form of grammar-based genetic programming, grammatical evolution (GE). Previous work has shown that simply adding modules to GE’s grammar has the potential to disrupt fit individuals developed by evolution up to that point. This paper presents a solution to prevent the disturbance in fitness that can come with modifying GE’s grammar with previously discovered modules. The results show an increase in performance from a previously examined grammar modification approach and also an increase in performance when compared to standard GE.
Science Foundation Ireland
ti, ke, ab, co - TS 26.04.12
http://hdl.handle.net/10197/3612
Marked
Mark
A parametric model for spectral sound synthesis of musical sounds
(2008)
Kreutzer, Cornelia; Walker, Jacqueline; O'Neill, Michael
A parametric model for spectral sound synthesis of musical sounds
(2008)
Kreutzer, Cornelia; Walker, Jacqueline; O'Neill, Michael
Abstract:
We introduce a reduced parameter synthesis model for the spectral synthesis of musical sounds, which preserves the timbre and the naturalness of the musical sound. It also provides large flexibility for the user and reduces the number of synthesis parameters compared to traditional analysis/re-synthesis methods. The proposed model is almost completely independent from a previous spectral analysis. We present a frequency estimation method using a random walk to keep the naturalness of the sound without using a separate noise model. Three different approaches have been tested to estimate the amplitude values for the synthesis, namely, local optimization, the use of a lowpass filter and polynomial fitting. All of these approaches give good results, especially for the sustain part of the signal.
SFI
http://hdl.handle.net/10344/143
Marked
Mark
A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling
(2012)
Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling
(2012)
Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
Abstract:
EvoFIN 2011, 5th European Event on Evolutionary and Natural Computation in Finance and Economics in EvoApplications, Torino, Italy, 27-29 April 2011
This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the ...
http://hdl.handle.net/10197/3655
Marked
Mark
A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling
(2011)
Tuite, Cliodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
A preliminary investigation of overfitting in evolutionary driven model induction : implications for financial modelling
(2011)
Tuite, Cliodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
Abstract:
EvoStar 2011, 27-29 April, 2011, Torino Italy
This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the above criterion increases the extrapolation abilities of symbolic regression models, but is by no means...
http://hdl.handle.net/10197/3059
Marked
Mark
A preliminary investigation of overfitting in evolutionary driven model induction: implications for financial modelling
(2012)
Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
A preliminary investigation of overfitting in evolutionary driven model induction: implications for financial modelling
(2012)
Tuite, Clíodhna; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
Abstract:
Paper presented at EvoFin 2011 5th European Event on Evolutionary and Naturak Computation in Finance and Economics, Torino, Italy, April 27-29, 2011
This paper investigates the effects of early stopping as a method to counteract overfitting in evolutionary data modelling using Genetic Programming. Early stopping has been proposed as a method to avoid model overtraining, which has been shown to lead to a significant degradation of out-of-sample performance. If we assume some sort of performance metric maximisation, the most widely used early training stopping criterion is the moment within the learning process that an unbiased estimate of the performance of the model begins to decrease after a strictly monotonic increase through the earlier learning iterations. We are conducting an initial investigation on the effects of early stopping in the performance of Genetic Programming in symbolic regression and financial modelling. Empirical results suggest that early stopping using the ...
http://hdl.handle.net/10197/3466
Marked
Mark
A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
(2012)
Hemberg, Erik; Ho, Lester; O'Neill, Michael; Claussen, Holger
A symbolic regression approach to manage femtocell coverage using grammatical genetic programming
(2012)
Hemberg, Erik; Ho, Lester; O'Neill, Michael; Claussen, Holger
Abstract:
Paper presented at the ACM Genetic and Evolutionary Computation Conference GECCO 2011 Symbolic Regression and Modelling Workshop, Dublin, Ireland, 12-16, July
We present a novel application of Grammatical Evolution to the real-world application of femtocell coverage. A symbolic regression approach is adopted in which we wish to uncover an expression to automatically manage the power settings of individual femtocells in a larger femtocell group to optimise the coverage of the network under time varying load. The generation of symbolic expressions is important as it facilitates the analysis of the evolved solutions. Given the multi-objective nature of the problem we hybridise Grammatical Evolution with NSGA-II connected to tabu search. The best evolved solutions have superior power consumption characteristics than a fixed coverage femtocell deployment.
Science Foundation Ireland
Conference website
http://www.sigevo.org/gecco-2011/
ti, ke, ab, co, li - TS 02.12
http://hdl.handle.net/10197/3511
Marked
Mark
Acceleration of grammatical evolution using graphics processing units
(2012)
Pospichal, Petr; Muphy, Eoin; O'Neill, Michael; Schwarz, Josef; Jaros, Jiri
Acceleration of grammatical evolution using graphics processing units
(2012)
Pospichal, Petr; Muphy, Eoin; O'Neill, Michael; Schwarz, Josef; Jaros, Jiri
Abstract:
Presented at the CIGPU Workshop at GECCO '11, the 13th annual conference companion on Genetic and evolutionary computation, Dublin, Ireland, 12-16, July 2011
Several papers show that symbolic regression is suitable for data analysis and prediction in financial markets. Grammatical Evolution (GE), a grammar-based form of Genetic Programming (GP), has been successfully applied in solving various tasks including symbolic regression. However, often the computational effort to calculate the fitness of a solution in GP can limit the area of possible application and/or the extent of experimentation undertaken. This paper deals with utilizing mainstream graphics processing units (GPU) for acceleration of GE solving symbolic regression. GPU optimization details are discussed and the NVCC compiler is analyzed. We design an effective mapping of the algorithm to the CUDA framework, and in so doing must tackle constraints of the GPU approach, such as the PCI-express bottleneck and main ...
http://hdl.handle.net/10197/3545
Marked
Mark
An analysis of genotype-phenotype maps in grammatical evolution
(2010)
Fagan, David; O'Neill, Michael; Galván-López, Edgar; Brabazon, Anthony; McGarraghy...
An analysis of genotype-phenotype maps in grammatical evolution
(2010)
Fagan, David; O'Neill, Michael; Galván-López, Edgar; Brabazon, Anthony; McGarraghy, Sean
Abstract:
European Conference on Genetic Programming, Istanbul Turkey, 7-9 April, 2010
We present an analysis of the genotype-phenotype map in Grammatical Evolution (GE). The standard map adopted in GE is a depth-first expansion of the non-terminal symbols during the derivation sequence. Earlier studies have indicated that allowing the path of the expansion to be under the guidance of evolution as opposed to a de- terministic process produced significant performance gains on all of the benchmark problems analysed. In this study we extend this analysis to in- clude a breadth-first and random map, investigate additional benchmark problems, and take into consideration the implications of recent results on alternative grammar representations with this new evidence. We con- clude that it is possible to improve the performance of grammar-based Genetic Programming by the manner in which a genotype-phenotype map is performed.
Science Foundation Ireland
Embargo until April 2011 - AV 1/11/2...
http://hdl.handle.net/10197/2566
Marked
Mark
An efficient customer search tool within an anti-money laundering application implemented on an internaitonal bank's dataset
(2016)
Le-Khac, Nhien-An; Markos, Sammer; O'Neill, Michael; Brabazon, Anthony; Kechadi, T...
An efficient customer search tool within an anti-money laundering application implemented on an internaitonal bank's dataset
(2016)
Le-Khac, Nhien-An; Markos, Sammer; O'Neill, Michael; Brabazon, Anthony; Kechadi, Tahar
Abstract:
2009 International Conference on Information and Knowledge Engineering (IKE'09), Las Vegas, USA, 13-16 July 2009
Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nations. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most of the financial institutions internationally have been implementing anti-money laundering solutions (AML) to fight investment fraud activities. In AML, the customer identification is an important task which helps AML experts to monitor customer habits: some being customer domicile, transactions that they are involved in etc. However, simple query tools provided by current DBMS as well as naive approaches in customer searching may produce incorrect and ambiguous results and their processing time is also very high due to the complexity of the database system archite...
http://hdl.handle.net/10197/7846
Marked
Mark
An examination on the modularity of grammars in grammatical evolutionary design
(2010)
Swafford, John Mark; O'Neill, Michael
An examination on the modularity of grammars in grammatical evolutionary design
(2010)
Swafford, John Mark; O'Neill, Michael
Abstract:
IEEE World Congress on Computational Intelligence,Barcelona, Spain, 18-23 July.
This work furthers the understanding of modularity in grammar-based genetic programming approaches by analyzing how different grammars may be capable of producing the same phenotypes, but still display differences in performance on the same problems. This is done by creating four grammars with varying levels of modularity and using them with grammatical evolution to evolve floor plan designs. The results of this experimentation show how increases in modularity, brought about by simple modifications in the grammars, and increases in the quality of solutions go hand in hand. It also demonstrates how more modular grammars explore more individuals even while fitness remains the same or changes in only minor increments.
Science Foundation Ireland
ti, ke - AS 04/11/2010
http://hdl.handle.net/10197/2544
Marked
Mark
An exploration of genetic algorithms for efficient musical instrument identification
(2009)
Loughran, Róisín; Walker, Jacqueline; O'Neill, Michael
An exploration of genetic algorithms for efficient musical instrument identification
(2009)
Loughran, Róisín; Walker, Jacqueline; O'Neill, Michael
Abstract:
This study explores the use of genetic algorithms (GA) in optimising feature selection for musical instrument recognition. 95 timbral features were used to classify 3006 musical instrument samples into 5 instrument groups. A GA was used to optimise the best selection of features to use with an multi-layered perceptron (MLP) to classify the instruments. Of all the features examined, the Centroid Evolution was found to be the most important. The system was run a number of times with varying numbers of features as determined by the GA. The accuracy of the classi er was not reduced with a reduction in features, indicating that the GA successfully determined the best features to use.
ACCEPTED
peer-reviewed
http://hdl.handle.net/10344/6822
Marked
Mark
Applying Genetic Regulatory Networks to Index Trading
(2016)
Nicolau, Miguel; O'Neill, Michael; Brabazon, Anthony
Applying Genetic Regulatory Networks to Index Trading
(2016)
Nicolau, Miguel; O'Neill, Michael; Brabazon, Anthony
Abstract:
12th International Conference on Parallel Problem Solving from Nature, Taormina, Italy, 1-5 September 2012
This paper explores the computational power of genetic regulatory network models, and the practicalities of applying these to real-world problems. The specific domain of financial trading is tackled; this is a problem where time-dependent decisions are critical, and as such benefits from the differential gene expression that these networks provide. The results obtained are on par with the best found in the literature, and highlight the applicability of these models to this type of problem.
Science Foundation Ireland
http://hdl.handle.net/10197/8148
Marked
Mark
Combining structural analysis and multi-objective criteria for evolutionary architectural design
(2012)
Byrne, Jonathan; Fenton, Michael; Hemberg, Erik; McDermott, James; O'Neill, Michae...
Combining structural analysis and multi-objective criteria for evolutionary architectural design
(2012)
Byrne, Jonathan; Fenton, Michael; Hemberg, Erik; McDermott, James; O'Neill, Michael; Shotton, Elizabeth; McNally, Ciaran
Abstract:
EvoMUSART 2011, 9th European Event on Evolutionary and Biologically Inspired Music, Sound, Art and Design in EvoApplications, Torino, Italy, Apr 27-29, 2011
This study evolves and categorises a population of conceptual designs by their ability to handle physical constraints. The design process involves a trade-off between form and function. The aesthetic considerations of the designer are constrained by physical considerations and material cost. In previous work, we developed a design grammar capable of evolving aesthetically pleasing designs through the use of an interactive evolutionary algorithm. This work implements a fitness function capable of applying engineering objectives to automatically evaluate designs and, in turn, reduce the search space that is presented to the user.
Science Foundation Ireland
ti, ke, ab, co, de, li - TS 23.02.12
http://hdl.handle.net/10197/3546
Marked
Mark
Comparing the performance of the evolvable πgrammatical evolution genotype-phenotype pap to grammatical evolution in the dynamic Ms. Pac-Man environment
(2010)
Galván-López, Edgar; Fagan, David; Murphy, Eoin; Swafford, John Mark; Agapitos, Alexand...
Comparing the performance of the evolvable πgrammatical evolution genotype-phenotype pap to grammatical evolution in the dynamic Ms. Pac-Man environment
(2010)
Galván-López, Edgar; Fagan, David; Murphy, Eoin; Swafford, John Mark; Agapitos, Alexandros; O'Neill, Michael; Brabazon, Anthony
Abstract:
IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
In this work, we examine the capabilities of two forms of mappings by means of Grammatical Evolution (GE) to successfully generate controllers by combining high-level functions in a dynamic environment. In this work we adopted the Ms. Pac-Man game as a benchmark test bed. We show that the standard GE mapping and Position Independent GE (πGE) mapping achieve similar performance in terms of maximising the score. We also show that the controllers produced by both approaches have an overall better performance in terms of maximising the score compared to a hand-coded agent. There are, however, significant differences in the controllers produced by these two approaches: standard GE produces more controllers with invalid code, whereas the opposite is seen with πGE.
Science Foundation Ireland
http://hdl.handle.net/10197/2571
Marked
Mark
Comparison of features in musical instrument identification using artificial neural networks
(2008)
Loughran, Róisín; Walker, Jacqueline; O'Farrell, Marion; O'Neill, Michael
Comparison of features in musical instrument identification using artificial neural networks
(2008)
Loughran, Róisín; Walker, Jacqueline; O'Farrell, Marion; O'Neill, Michael
Abstract:
This paper examines the use of a number of auditory features in identifying musical instruments. The Temporal Envelope, Centroid, Melfrequency Cepstral Coefficients (MFCCs), Inharmonicity, Spectral Irregularity and Number of Spectral Peaks are all examined. By using these features to train a Multi-Layered Perceptron (MLP), it is determined that the MFCCs are the most efficient of these features in musical instrument identification. The Inharmonicity, Spectral Irregularity and Number of Spectral Peaks offered no benefit to the classifier. Of the instruments studied, the piano was most accurately classified and the violin was the least accurately classified instrument.
http://hdl.handle.net/10344/144
Marked
Mark
Deep Evolution of Feature Representations for Handwritten Digit Recognition
(2017)
Agapitos, Alexandros; O'Neill, Michael; Nicolau, Miguel; Fagan, David; Kattan, Ahm...
Deep Evolution of Feature Representations for Handwritten Digit Recognition
(2017)
Agapitos, Alexandros; O'Neill, Michael; Nicolau, Miguel; Fagan, David; Kattan, Ahmed; Curran, Kathleen M.
Abstract:
2015 IEEE Congress on Evolutionary Computation (CEC), Sendai, Japan, 25-28 May 2015
A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.
http://hdl.handle.net/10197/8274
Marked
Mark
Defining locality as a problem difficulty measure in genetic programming
(2012)
Galván-López, Edgar; McDermott, James; O'Neill, Michael; Brabazon, Anthony
Defining locality as a problem difficulty measure in genetic programming
(2012)
Galván-López, Edgar; McDermott, James; O'Neill, Michael; Brabazon, Anthony
Abstract:
A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bitstring-based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the g...
http://hdl.handle.net/10197/3512
Marked
Mark
Defining locality as a problem difficulty measure in genetic programming
(2011)
Galván López, Edgar; McDermott, James; O'Neill, Michael; Brabazon, Michael
Defining locality as a problem difficulty measure in genetic programming
(2011)
Galván López, Edgar; McDermott, James; O'Neill, Michael; Brabazon, Michael
Abstract:
A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bitstring-based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the g...
http://mural.maynoothuniversity.ie/12335/
Marked
Mark
Defining locality in genetic programming to predict performance
(2010)
Galván-López, Edgar; McDermott, James; O'Neill, Michael; Brabazon, Anthony
Defining locality in genetic programming to predict performance
(2010)
Galván-López, Edgar; McDermott, James; O'Neill, Michael; Brabazon, Anthony
Abstract:
Congress on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Barcelona, Spain, 18-23 July
A key indicator of problem difficulty in evolutionary computation problems is the landscape’s locality, that is whether the genotype-phenotype mapping preserves neighbourhood. In genetic programming the genotype and phenotype are not distinct, but the locality of the genotype- fitness mapping is of interest. In this paper we extend the original standard quantitative definition of locality to cover the genotype-fitness case, considering three possible definitions. By relating the values given by these definitions with the results of evolutionary runs, we investigate which definition is the most useful as a predictor of performance.
Science Foundation Ireland
Conference details
http://www.wcci2010.org/
ti, ke, ab OR 15/11/2010
http://hdl.handle.net/10197/2559
Marked
Mark
Development of low-dose protocols for thin-section CT assessment of cystic fibrosis in pediatric patients.
(2010)
O'Connor, Owen J.; Vandeleur, Moya; McGarrigle, Anne Marie; Moore, Niamh; McWillia...
Development of low-dose protocols for thin-section CT assessment of cystic fibrosis in pediatric patients.
(2010)
O'Connor, Owen J.; Vandeleur, Moya; McGarrigle, Anne Marie; Moore, Niamh; McWilliams, Sebastian R.; McSweeney, Seán E.; O'Neill, Michael; Ní Chróinín, Muireann; Maher, Michael M.
Abstract:
Purpose: To develop low-dose thin-section computed tomographic (CT) protocols for assessment of cystic fibrosis (CF) in pediatric patients and determine the clinical usefulness thereof compared with chest radiography. Materials and Methods: After institutional review board approval and informed consent from patients or guardians were obtained, 14 patients with CF and 11 patients without CF (16 male, nine female; mean age, 12.6 years ± 5.4 [standard deviation]; range, 3.5–25 years) who underwent imaging for clinical reasons underwent low-dose thin-section CT. Sections 1 mm thick (protocol A) were used in 10 patients, and sections 0.5 mm thick (protocol B) were used in 15 patients at six levels at 120 kVp and 30–50 mA. Image quality and diagnostic acceptability were scored qualitatively and quantitatively by two radiologists who also quantified disease severity at thin-section CT and chest radiography. Effective doses were calculated by using a CT dosimetry calculator. Results: Low-do...
http://hdl.handle.net/10468/6635
Marked
Mark
Dynamic ant : introducing a new benchmark for genetic programming in dynamic environments
(2012)
Fagan, David; Nicolau, Miguel; Hemberg, Erik; O'Neill, Michael; Brabazon, Anthony
Dynamic ant : introducing a new benchmark for genetic programming in dynamic environments
(2012)
Fagan, David; Nicolau, Miguel; Hemberg, Erik; O'Neill, Michael; Brabazon, Anthony
Abstract:
In this paper we present a new variant of the ant problem in the dynamic problem domain. This approach presents a functional dynamism to the problem landscape, where by the behaviour of the ant is driven by its ability to explore the search space being constrained. This restriction is designed in such a way as to ensure that no generalised solution to the problem is possible, thus providing a functional change in behaviour.
Science Foundation Ireland
ti, ke, ab - TS 28.03.12 (Orna, later version presented at conference, see copyright field).
http://hdl.handle.net/10197/3570
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