Most current research into computers and music focus on the development of media technology for delivering music to consumers (e.g., MP3 format, Internet search engines, and so on) our research focuses on the development of technology for musical creativity. This paper focuses on a particular technology that we are developing, which is based on Artificial Life (A-Life). The Artificial Life (A-life) approach to the development of software for music is a promising new development. However, the vast majority of existing A-life-based systems for musical composition employ Genetic Algorithms (GA) to produce musical melodies, rhythms, and so on. In these systems, music parameters are represented as genotypes and GA operators are applied on these representations to produce music according to given fitness criteria. We suggest that strictly GA-based methods suffer from the fact that musical composition should not be constrained by a definite set of fitness criteria. Moreover music is largely a cultural phenomenon driven by social pressure and this is cumbersome to model with standard GA alone. We propose an alternative approach to using strictly GA-based methods: the design of evolutionary algorithms that consider music as a cultural phenomenon whereby social pressure plays an important role in the development of musical conventions. This paper introduces three of algorithms of our own design: popularity, transformation and complexity algorithms, respectively. We also devised tools for extracting information about the behaviour of the algorithms in many different ways, providing the means to study the outcomes systematically.