Models of Genetic Programming (GP) frequently reflect a neo-Darwinian view to evolution in which inheritance is based on a process of gradual refinement and the resulting solutions take the form of single monolithic programs. Conversely, introducing an explicitly symbiotic model of inheritance makes a divide-and-conquer metaphor for problem decomposition central to evolution. Benchmarking gradualist versus symbiotic models of evolution under a common evolutionary framework illustrates that not only does symbiosis result in more accurate solutions, but the solutions are also much simpler in terms of instruction and attribute count over a wide range of classification problem domains. 1

In 1996 Daida et al. reviewed the case for using symbiosis as the basis for evolving complex adaptive systems [6]. Specific observations included the impact of different philosophical views taken by biologists as to what constituted a symbiotic relationship and whether symbiosis represented an operator or a state. The case was made for symbiosis as an operator. Thus, although specific cost benefit characterizations may vary, the underlying process of symbiosis is the same, supporting the operator based perspective. Symbiosis provides an additional mechanism for adaption/ complexification than available under Mendelian genetics with which Evolutionary Computation (EC) is most widely associated. In the following we review the case for symbiosis in EC. In particular, symbiosis appears to represent a much more effective mechanism for automatic hierarchical model building and therefore scaling EC methods to more difficult problem domains than through Mendelian genetics alone.

. GPmuse is software which explores one connection between computation and creativity using a symbiosis-inspired genetic programming paradigm in which distinct agents collaborate to produce 16th-century counterpoint. 1. Introduction In a recent popular article on the science of creativity [3], Margaret Boden describes two well-known computer programs that produce music in the styles of different composers. That a computer can at least appear to be creative suggests that there may exist profound connections between computation and creativity. Consideration of such connections involves the asking of what Boden has called "Lovelace-questions," (see [2]) one of which has bearing on this paper. This "Lovelacequestion " asks whether computational ideas can help us understand how human creativity is possible. The authors of this paper answer with Boden in the affirmative. Music of the 16th-century owes much to computation: the rules and conventions of the pre-Baroque may seem suffocati...

This chapter addresses the question “what is a building block in genetic programming? ” by examining the smallest subtree possible—a single leaf node. The analysis of these subtrees indicates a considerably more complex portrait of what exactly is meant by a building block in GP than what has traditionally been considered. What is a building block in genetic programming (GP)? Intuitively, we might answer simple pieces of code, subprograms, that GP uses to build more complex programs. Intuitively, too, that idea resonates with some theoretical developments in genetic algorithms. Some