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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
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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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Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.