9 Result(s)
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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
Cable Function Analysis for the Musculoskeletal Static Workspace of a Human Shoulder
The study of cable function allows the particular cables towards generation of to determined for cable-driven manipulators (CDPMs). This study is fundamental in the understanding of the arrangement o...
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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
On the Task Specific Evaluation and Optimisation of Cable-Driven Manipulators
Cable-driven manipulators are traditionally designed for general performance objectives, such as maximisation of workspace. To take advantage of the reconfigurability of cable-driven mechanisms, the optimisati...
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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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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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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...
