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The nature and source of evolutionary trends in complexity is difficult to assess from the fossil record, and the driven vs. passive nature of such trends has been debated for decades. There are also questions about how effectively artificial life software can evolve increasing levels of complexity. We extend our previous work demonstrating an evolutionary increase in an information theoretic measure of neural complexity in an artificial life system (Polyworld), and introduce a new technique for distinguishing driven from passive trends in complexity. Our experiments show that evolution can and does select for complexity increases in a driven fashion, in some circumstances, but under other conditions it can also select for complexity stability. It is suggested that the evolution of complexity is entirely driven---just not in a single direction---at the scale of species. This leaves open the question of evolutionary trends at larger scales., Comment: 8 pages; In Bullock, S. et al. eds. Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems. MIT Press. Cambridge, MA. 2008

Development plays a significant role in biological evolution, and is likely to prove an effective route to overcoming the limitations of direct genotype-phenotype mappings in artificial evolution. Nonetheless, the relationship between development and evolution is complex and still poorly understood. One question of current interest concerns the possible role that developmental processes may play in orienting evolution. A first step towards exploring this issue from a theoretical perspective is understanding the structure of ontogenetic space: the space of possible genotype-phenotype mappings. Using a quantitative model of development that enables ontogenetic space to be characterised in terms of complexity, we show that ontogenetic landscapes have a characteristic structure that varies with genotypic properties.

Within the context of Artificial Life the question about the role of modularity has turned out to be crucial, especially with regard to the problem of evolvability. In order to be able to observe the development of modular structure, appropriate modularity measures are important. We introduce a continuous measure based on information theory which can characterize the coupling among subsystems in a search problem. In order to illustrate the concepts developed, they are applied to a very simple and intuitive set of combinatorial problems similar to scenarios used in the seminal work by Simon (1969). It is shown that this measure is closely related to the classification of search problems in terms of Separability, Non-Decomposability and Modular Interdependency as introduced in (Watson and Pollack, 2005).

A computer simulation model, can produce some interesting and surprising results which one would not expect from initial analysis of the algorithm and data. We question however, whether the description of such a computer simulation modelling procedure (data + algorithm + results) can constitute an explanation as to why the algorithm produces such an effect. Specifically, in the field of theoretical biology, can such a procedure constitute real scientific explanation of biological phenomena? We compare computer simulation modelling to explicit mathematical treatment concluding that there are fundamental differences between the two. Since computer simulations can model systems that mathematical models can not, we look at ways of improving explanatory power of computer simulations through empirical style study and mechanistic decomposition.

Artificial Life is an interdisciplinary effort to investigate the fundamental properties of living systems through the simulation and synthesis of life-like processes in artificial media. The field brings a powerful set of tools to the study of how high-level behavior can arise in systems governed by simple rules of interaction. This tenth volume marks two decades of research in this interdisciplinary scientific comunity, a period marked by vast advances in the life sciences. The field has contributed fundamentally to our understanding of life itself through computer models, and has led to novel solutions to complex real-world problems -- from disease prevention to stock market prediction -- across high technology and human society. The proceedings of the biennial A-life conference -- which has grown over the years from a small workshop in Santa Fe to a major international meeting -- reflect the increasing importance of the work to all areas of contemporary science.