Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis

Conventionally, evolution by natural selection is almost inseparable from the notion of accumulating successive slight variations. Although it has been suggested that symbiotic mechanisms that combine together existing entities provide an alternative to gradual, or 'accretive', evolutionary change, there has been disagreement about what impact these mechanisms have on our understanding of evolutionary processes. Meanwhile, in artificial evolution methods used in computer science, it has been suggested that the composition of genetic material under sexual recombination may provide adaptation that is not available under mutational variation, but there has been considerable difficulty in demonstrating this formally. Thus far, it has been unclear what types of systems, if any, can be evolved by such 'compositional' mechanisms that cannot be evolved by accretive mechanisms. This dissertation takes an interdisciplinary approach to this question by building abstract computational simulations of accretive and compositional mechanisms. We identify a class of complex systems possessing 'modular interdependency', incorporating highly epistatic but modular substructure. This class typifies characteristics that are pathological for accretive evolution - the corresponding fitness landscape is highly rugged, has many local optima creating broad fitness saddles, and includes 'irreducibly complex' adaptations that cannot be reached by a succession of gradually changing proto-systems. Nonetheless, we provide simulations to show that this class of system is easily evolvable under sexual recombination or a mechanism of 'symbiotic encapsulation'. Our simulations and analytic results help us to understand the fundamental differences in the adaptive capacities of these mechanisms, and the conditions under which they provide an adaptive advantage. These models exemplify how certain kinds of complex systems, considered unevolvable under normal accretive change, are, in principle, easily evolvable under compositional evolution

Separating what is evaluated from what is selected in artificial evolution

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

Reticulate Evolution: Symbiogenesis, Lateral Gene Transfer, Hybridization and Infectious heredity

info:eu-repo/semantics/publishedVersio

Epidemiology and Evolution of Fungal Pathogens in Plants and Animals

International audienc

The Evolution of Diversity

Since the beginning of time, the pre-biological and the biological world have seen a steady increase in complexity of form and function based on a process of combination and re-combination. The current modern synthesis of evolution known as the neo-Darwinian theory emphasises population genetics and does not explain satisfactorily all other occurrences of evolutionary novelty. The authors suggest that symbiosis and hybridisation and the more obscure processes such as polyploidy, chimerism and lateral transfer are mostly overlooked and not featured sufficiently within evolutionary theory. They suggest, therefore, a revision of the existing theory including its language, to accommodate the scientific findings of recent decades

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field