Marcus Gallagher | The University of Queensland, Australia (original) (raw)

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Papers by Marcus Gallagher

Research paper thumbnail of Optimization and Probabilistic Modelling

Evolutionary algorithms perform optimization using a population of sample solution points. An int... more Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis ...

Research paper thumbnail of Convergence analysis of UMDA C with finite populations: a case study on flat landscapes

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, Jul 8, 2009

Research paper thumbnail of An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 2009

ABSTRACT Success rate is a commonly adopted performance criterion for evaluating Evolutionary Alg... more ABSTRACT Success rate is a commonly adopted performance criterion for evaluating Evolutionary Algorithms due to their inherent randomness. However, the classical large-sample binomial test based on normal distributions is only valid with a relatively large number of trials, which ...

Research paper thumbnail of Faster and parameter-free discord search in quasi-periodic time series

Proceedings of the 15th Pacific Asia Conference on Advances in Knowledge Discovery and Data Mining Volume Part Ii, 2011

Research paper thumbnail of A hybrid approach to parameter tuning in genetic algorithms

2005 Ieee Congress on Evolutionary Computation Vols 1 3 Proceedings, Oct 2, 2005

Research paper thumbnail of Bayesian inference in estimation of distribution algorithms

2007 Ieee Congress on Evolutionary Computation Vols 1 10 Proceedings, Sep 1, 2007

Research paper thumbnail of Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms

Lecture Notes in Computer Science, 2004

Research paper thumbnail of Convergence analysis of UMDA

Research paper thumbnail of A Mathematical Modelling Technique for the Analysis of the Dynamics of a Simple Continuous EDA

2006 IEEE International Conference on Evolutionary Computation, 2006

Research paper thumbnail of Experimental Results for the Special Session on Real-Parameter Optimization at CEC 2005: A Simple, Continuous EDA

2005 IEEE Congress on Evolutionary Computation, 2005

Research paper thumbnail of On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

Research paper thumbnail of On the importance of diversity maintenance in estimation of distribution algorithms

Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05, 2005

Research paper thumbnail of Combining meta-EAs and racing for difficult EA parameter tuning tasks

Research paper thumbnail of Bayesian inference in estimation of distribution algorithms

Research paper thumbnail of Unsupervised DRG upcoding detection in healthcare databases

Research paper thumbnail of Faster and parameter-free discord search in quasi-periodic time series

Research paper thumbnail of Parameter-Free Search of Time-Series Discord

Journal of Computer Science and Technology, 2013

ABSTRACT Time-series discord is widely used in data mining applications to characterize anomalous... more ABSTRACT Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm's applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.

Research paper thumbnail of A general-purpose tunable landscape generator

IEEE Transactions on Evolutionary Computation, 2000

Research paper thumbnail of Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift

Evolutionary Computation, 2005

Research paper thumbnail of Using Gaussian Process with Test Rejection to Detect T-Cell Epitopes in Pathogen Genomes

Research paper thumbnail of Optimization and Probabilistic Modelling

Evolutionary algorithms perform optimization using a population of sample solution points. An int... more Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis ...

Research paper thumbnail of Convergence analysis of UMDA C with finite populations: a case study on flat landscapes

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, Jul 8, 2009

Research paper thumbnail of An improved small-sample statistical test for comparing the success rates of evolutionary algorithms

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, 2009

ABSTRACT Success rate is a commonly adopted performance criterion for evaluating Evolutionary Alg... more ABSTRACT Success rate is a commonly adopted performance criterion for evaluating Evolutionary Algorithms due to their inherent randomness. However, the classical large-sample binomial test based on normal distributions is only valid with a relatively large number of trials, which ...

Research paper thumbnail of Faster and parameter-free discord search in quasi-periodic time series

Proceedings of the 15th Pacific Asia Conference on Advances in Knowledge Discovery and Data Mining Volume Part Ii, 2011

Research paper thumbnail of A hybrid approach to parameter tuning in genetic algorithms

2005 Ieee Congress on Evolutionary Computation Vols 1 3 Proceedings, Oct 2, 2005

Research paper thumbnail of Bayesian inference in estimation of distribution algorithms

2007 Ieee Congress on Evolutionary Computation Vols 1 10 Proceedings, Sep 1, 2007

Research paper thumbnail of Statistical Racing Techniques for Improved Empirical Evaluation of Evolutionary Algorithms

Lecture Notes in Computer Science, 2004

Research paper thumbnail of Convergence analysis of UMDA

Research paper thumbnail of A Mathematical Modelling Technique for the Analysis of the Dynamics of a Simple Continuous EDA

2006 IEEE International Conference on Evolutionary Computation, 2006

Research paper thumbnail of Experimental Results for the Special Session on Real-Parameter Optimization at CEC 2005: A Simple, Continuous EDA

2005 IEEE Congress on Evolutionary Computation, 2005

Research paper thumbnail of On building a principled framework for evaluating and testing evolutionary algorithms: A continuous landscape generator

Research paper thumbnail of On the importance of diversity maintenance in estimation of distribution algorithms

Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05, 2005

Research paper thumbnail of Combining meta-EAs and racing for difficult EA parameter tuning tasks

Research paper thumbnail of Bayesian inference in estimation of distribution algorithms

Research paper thumbnail of Unsupervised DRG upcoding detection in healthcare databases

Research paper thumbnail of Faster and parameter-free discord search in quasi-periodic time series

Research paper thumbnail of Parameter-Free Search of Time-Series Discord

Journal of Computer Science and Technology, 2013

ABSTRACT Time-series discord is widely used in data mining applications to characterize anomalous... more ABSTRACT Time-series discord is widely used in data mining applications to characterize anomalous subsequences in time series. Compared to some other discord search algorithms, the direct search algorithm based on the recurrence plot shows the advantage of being fast and parameter free. The direct search algorithm, however, relies on quasi-periodicity in input time series, an assumption that limits the algorithm's applicability. In this paper, we eliminate the periodicity assumption from the direct search algorithm by proposing a reference function for subsequences and a new sampling strategy based on the reference function. These measures result in a new algorithm with improved efficiency and robustness, as evidenced by our empirical evaluation.

Research paper thumbnail of A general-purpose tunable landscape generator

IEEE Transactions on Evolutionary Computation, 2000

Research paper thumbnail of Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift

Evolutionary Computation, 2005

Research paper thumbnail of Using Gaussian Process with Test Rejection to Detect T-Cell Epitopes in Pathogen Genomes

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