In artificial evolution, selection and evaluation are separate and distinct steps. This distinction is rather different in natural evolution, where fitness (corresponding to evaluation) is a direct consequence of selection rather than a precursor to it. This thesis presents a new way of thinking about artificial evolution that separates evaluation and selection and consequently opens up the space of potential evolutionary algorithms beyond the limitations imposed by ignoring this distinction. In Part I of the thesis we explore how varying the level of evaluation and selection impacts evolution. Using novel genetic algorithms (GAs) we show how group level evaluation allows evolution to find solutions to problems that require niching or a division of labour amongst component parts, something that cannot be accomplished using a standard GA. One of the inspirations for testing GAs with group-level evaluation was recent research into bacterial evolution which shows in bacterial colonies, distinguishing between the individual and group is very difficult because of the symbiotic relationship between different bacteria. We find that depending on the task it sometimes makes sense to select the individual while in other cases simply selecting groups is the best choice. Finally, we present a method for evolving the group size in these types of GAs that has the benefit of avoiding the need to know the optimal division of labour ahead of time. In Part II we move away from studying the relationship between evaluation and selection to show how our novel view of evolution can be used to develop GAs that implement horizontal gene transfer which was again inspired by looking at bacterial evolution. By testing these GAs on a variety of different tasks we show how this promiscuous gene swapping is often beneficial to evolution because it can reduce the probability of the population getting stuck on a sub-optimal solution. The thesis demonstrates the benefits of of looking at artificial evolution in terms of both evaluation and selection when it comes to algorithm development, and thus provides the GA community with a new context in which they can choose different algorithms appropriate to different tasks.

This thesis considers the relationship between adaptive behaviour and metabolism, using theoretical arguments supported by computational models to demonstrate mechanisms of adaptation that are uniquely available to systems based upon the metabolic organisation of self-production. It is argued how, by being sensitive to their metabolic viability, an organism can respond to the quality of its environment with respect to its metabolic well-being. This makes possible simple but powerful ‘self-sensitive' adaptive behaviours such as “If I am healthy now, keep doing the same as I have been doing – otherwise do something else.” This strategy provides several adaptive benefits, including the ability to respond appropriately to phenomena never previously experienced by the organism nor by any of its ancestors; the ability to integrate different environmental influences to produce an appropriate response; and sensitivity to the organism's present context and history of experience. Computational models are used to demonstrate these capabilities, as well as the possibility that self-sensitive adaptive behaviour can facilitate the adaptive evolution of populations of self-sensitive organisms through (i) processes similar to the Baldwin effect, (ii) increasing the likelihood of speciation events, and (iii) automatic behavioural adaptation to changes in the organism itself (such as genetic changes). In addition to these theoretical contributions, a computational model of self-sensitive behaviour is presented that recreates chemotaxis patterns observed in bacteria such as Azospirillum brasilense and Campylobacter jejuni. The models also suggest new explanations for previously unexplained asymmetric distributions of bacteria performing aerotaxis. More broadly, the work advocates further research into the relationship between behaviour and the metabolic organisation of self-production, an organisational property shared by all life. It also acts as an example of how abstract models that target theoretical concepts rather than natural phenomena can play a valuable role in the scientific endeavour.