Metrics, whether collected statically or dynamically, and whether constructed from source code, systems or processes, are largely regarded as a means of evaluating some property of interest. This viewpoint has been very successful in developing a body of knowledge, theory and experience in the application of metrics to estimation, predication, assessment, diagnosis, analysis and improvement. This paper shows that there is an alternative, complementary, view of a metric: as a fitness function, used to guide a search for optimal or near optimal individuals in a search space of possible solutions. This 'Metrics as Fitness Functions' (MAFF) approach offers a number of additional benefits to metrics research and practice because it allows metrics to be used to improve software as well as to assess it and because it provides an additional mechanism of metric analysis and validation. This paper presents a brief survey of search-based approaches and shows how metrics have been combined with the search based techniques to improve software systems. It describes the properties of a metric which make it a good fitness function and explains the benefits for metric analysis and validation which accrue from the MAFF approach.