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11 Result(s)

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  1. No Access

    Chapter and Conference Paper

    A Memetic Cooperative Co-evolution Model for Large Scale Continuous Optimization

    Cooperative co-evolution (CC) is a framework that can be used to ‘scale up’ EAs to solve high dimensional optimization problems. This approach employs a divide and conquer strategy, which decomposes a high dim...

    Yuan Sun, Michael Kirley, Saman K. Halgamuge in Artificial Life and Computational Intellig… (2017)

  2. No Access

    Chapter and Conference Paper

    A Meta-learning Prediction Model of Algorithm Performance for Continuous Optimization Problems

    Algorithm selection and configuration is a challenging problem in the continuous optimization domain. An approach to tackle this problem is to develop a model that links landscape analysis measures and algorit...

    Mario A. Muñoz, Michael Kirley… in Parallel Problem Solving from Nature - PPS… (2012)

  3. No Access

    Article

    Structure adaptation of hierarchical knowledge-based classifiers

    This paper introduces a new method to identify the qualified rule-relevant nodes to construct hierarchical neuro-fuzzy systems (HNFSs). After learning, the proposed method analyzes the entire history of activi...

    Waratt Rattasiri, Saman K. Halgamuge… in Neural Computing and Applications (2009)

  4. No Access

    Chapter

    Dynamic Self-Organising Maps: Theory, Methods and Applications

    In an effort to counter the restrictions enforced by the fixed map size and aspect ratio of a Kohonen Self-Organising Map, many variants to the method have been proposed. As a recent development, the Dynamic S...

    Arthur L. Hsu, Isaam Saeed… in Foundations of Computational, Intelligence… (2009)

  5. No Access

    Chapter and Conference Paper

    Information Theoretic Classification of Problems for Metaheuristics

    This paper proposes a model for metaheuristic research which recognises the need to match algorithms to problems. An empirical approach to producing a map** from problems to algorithms is presented. This map...

    Kent C. B. Steer, Andrew Wirth, Saman K. Halgamuge in Simulated Evolution and Learning (2008)

  6. No Access

    Article

    Polynomial kernel adaptation and extensions to the SVM classifier learning

    Three extensions to the Kernel-AdaTron training algorithm for Support Vector Machine classifier learning are presented. These extensions allow the trained classifier to adhere more closely to the constraints i...

    Ramy Saad, Saman K. Halgamuge, Jason Li in Neural Computing and Applications (2008)

  7. No Access

    Chapter and Conference Paper

    Combining News and Technical Indicators in Daily Stock Price Trends Prediction

    Stock market prediction has always been one of the hottest topics in research, as well as a great challenge due to its complex and volatile nature. However, most of the existing methods neglect the impact from...

    Yuzheng Zhai, Arthur Hsu, Saman K Halgamuge in Advances in Neural Networks – ISNN 2007 (2007)

  8. No Access

    Chapter and Conference Paper

    Semi-supervised Learning of Dynamic Self-Organising Maps

    We present a semi-supervised learning method for the Growing Self-Organising Maps (GSOM) that allows fast visualisation of data class structure on the 2D network. Instead of discarding data with missing values...

    Arthur Hsu, Saman K. Halgamuge in Neural Information Processing (2006)

  9. No Access

    Chapter and Conference Paper

    Scalable Dynamic Self-Organising Maps for Mining Massive Textual Data

    Traditional text clustering methods require enormous computing resources, which make them inappropriate for processing large scale data collections. In this paper we present a clustering method based on the wo...

    Yu Zheng Zhai, Arthur Hsu, Saman K. Halgamuge in Neural Information Processing (2006)

  10. No Access

    Article

    Particle Swarm Optimisation for Protein Motif Discovery

    In this paper, a modified particle swarm optimisation algorithm is proposed for protein sequence motif discovery. Protein sequences are represented as a chain of symbols and a protein sequence motif is a short...

    Bill C. H. Chang, Asanga Ratnaweera… in Genetic Programming and Evolvable Machines (2004)

  11. No Access

    Chapter

    Mining of Labeled Incomplete Data Using Fast Dimension Partitioning

    Two Dimensional Partitioning Techniques are proposed in this paper for fast mining of labeled data with missing values. The first Dimensional Partitioning Technique (DPT1) generates a classifier model by the u...

    Bill C. H. Chang, Saman K. Halgamuge in Data Mining and Computational Intelligence (2001)