Tech Reports
Tech Report kmi-96-11 Abstract
Genotypic-Space Mapping: Population Visualization for Genetic Algorithms
Techreport ID: kmi-96-11
Date: 1996
Author(s): Trevor Collins
This paper presents one proposed method for representing the population data of Genetic Algorithms (GAs). Typical population data from GAs are large high-dimensional sets of binary, decimal, real or string, state values. This makes their representation by two or three spatial dimensions somewhat difficult. Several attempts at population visualization have been made but have failed to efficiently solve this high dimensional to 2/3 dimensional space mapping problem. The use of the proposed "Genotypic-Space Mapping" method is put forward as a solution to this problem. It provides a unique linear mapping of a high-dimensional population string to a pair of x,y and/or z co- ordinates, thus enabling each population to be displayed as a scatter-plot in two or three dimensional space.
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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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