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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...
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Chapter and Conference Paper
The Algorithm Selection Problem on the Continuous Optimization Domain
The problem of algorithm selection, that is identifying the most efficient algorithm for a given computational task, is non-trivial. Meta-learning techniques have been used successfully for this problem in par...
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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...
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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...
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Chapter
The Rationale Behind Seeking Inspiration from Nature
There are currently numerous heuristic algorithms for combinatorial optimisation problems which are commonly described as nature-inspired. Parallels can certainly be drawn between these algorithms and various ...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...