The appeal of metric evaluation of research impact has attracted considerable interest in recent times. Although the public at large and administrative bodies are much interested in the idea, scientists and other researchers are much more cautious, insisting that metrics are but an auxiliary instrument to the qualitative peer-based judgement. The goal of this article is to propose availing of such a well positioned construct as domain taxonomy as a tool for directly assessing the scope and quality of research. We first show how taxonomies can be used to analyse the scope and perspectives of a set of research projects or papers. Then we proceed to define a research team or researcher's rank by those nodes in the hierarchy that have been created or significantly transformed by the results of the researcher. An experimental test of the approach in the data analysis domain is described. Although the concept of taxonomy seems rather simplistic to describe all the richness of a research domain, its changes and use can be made transparent and subject to open discussions.
The established approach in genetic programming (GP) involves the definition of functions and terminals appropriate to the problem at hand, after which evolution of expressions using these definitions takes place. We have recently developed a system, dubbed FINCH (Fertile Darwinian Bytecode Harvester), to evolutionarily improve actual, extant software, which was not intentionally written for the purpose of serving as a GP representation in particular, nor for evolution in general. This is in contrast to existing work that uses restricted subsets of the Java bytecode instruction set as a representation language for individuals in genetic programming. The ability to evolve Java programs will hopefully lead to a valuable new tool in the software engineer's toolkit.
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