Avida: a software platform for research in computational evolutionary biology

Avida is a software platform for experiments with self-replicating and evolving computer programs. It provides detailed control over experimental settings and protocols, a large array of measurement tools, and sophisticated methods to analyze and post-process experimental data. We explain the general principles on which Avida is built, as well as its main components and their interactions. We also explain how experiments are set up, carried out, and analyzed

Complexity in artificial life

Master'sMASTER OF SCIENC

Studying the Evolvability of of Self-Encoding Genotype-Phenotype Maps

We introduce a model of reproduction in which the genotype-phenotype (G-P) map is able to evolve. In this model, Each or-ganism implements a G-P map, determining how the organism is encoded in its genome. Crucially, it also determines how the G-P map itself is encoded. We call these maps ‘self-encoding’. We relate this model to recent artificial life research, and back to the seminal work of John von Neumann. We simulate popu-lations of organisms that have as their genome and G-P map the axiom and production rules of an L-system. The pop-ulations are given the task of optimizing a dynamic fitness function. Our purpose is to study whether the self-encoding property has any effect on the evolution of evolvability, and to look for other factors that lead to the evolution of G-P maps that confer evolvability. We find that evolvability does evolve, but only when we add constraints to the model

Natural Selection, Adaptive Evolution and Diversity in Computational Ecosystems

The central goal of this thesis is to provide additional criteria towards implementing open-ended evolution in an artificial system. Methods inspired by biological evolution are frequently applied to generate autonomous agents too complex to design by hand. Despite substantial progress in the area of evolutionary computation, additional efforts are needed to identify a coherent set of requirements for a system capable of exhibiting open-ended evolutionary dynamics. The thesis provides an extensive discussion of existing models and of the major considerations for designing a computational model of evolution by natural selection. Thus, the work in this thesis constitutes a further step towards determining the requirements for such a system and introduces a concrete implementation of an artificial evolution system to evaluate the developed suggestions. The proposed system improves upon existing models with respect to easy interpretability of agent behaviour, high structural freedom, and a low-level sensor and effector model to allow numerous long-term evolutionary gradients. In a series of experiments, the evolutionary dynamics of the system are examined against the set objectives and, where appropriate, compared with existing systems. Typical agent behaviours are introduced to convey a general overview of the system dynamics. These behaviours are related to properties of the respective agent populations and their evolved morphologies. It is shown that an intuitive classification of observed behaviours coincides with a more formal classification based on morphology. The evolutionary dynamics of the system are evaluated and shown to be unbounded according to the classification provided by Bedau and Packard’s measures of evolutionary activity. Further, it is analysed how observed behavioural complexity relates to the complexity of the agent-side mechanisms subserving these behaviours. It is shown that for the concrete definition of complexity applied, the average complexity continually increases for extended periods of evolutionary time. In combination, these two findings show how the observed behaviours are the result of an ongoing and lasting adaptive evolutionary process as opposed to being artifacts of the seeding process. Finally, the effect of variation in the system on the diversity of evolved behaviour is investigated. It is shown that coupling individual survival and reproductive success can restrict the available evolutionary trajectories in more than the trivial sense of removing another dimension, and conversely, decoupling individual survival from reproductive success can increase the number of evolutionary trajectories. The effect of different reproductive mechanisms is contrasted with that of variation in environmental conditions. The diversity of evolved strategies turns out to be sensitive to the reproductive mechanism while being remarkably robust to the variation of environmental conditions. These findings emphasize the importance of being explicit about the abstractions and assumptions underlying an artificial evolution system, particularly if the system is intended to model aspects of biological evolution

Modelling evolution of genome size in prokaryotes in response to changes in their abiotic environment

The size of the genomes of known free-living prokaryotes varies from � 1:3 Mbp to � 13 Mbp. This thesis proposes a possible explanation of this variation due to variability of the physical conditions of the environment. In a stable environment, competition for the resource becomes the main force of selection and smaller (thus cheaper) genomes are favoured. In more variable conditions larger genomes will be preferred, as they have a wider range of response to a less predictable environment. An agent-based model (ABM) of genome evolution in an free-living prokaryotic population has been proposed. Using the classic Hutchinson niche space model, a gene was defined as a Gaussian function over a corresponding niche dimension. The cell can have more than one gene along a given dimension, and the envelope of all the corresponding responses is considered a full description of a cell’s phenotype over that dimension. Gene deletion, gene duplication, and modifying mutations are permitted during reproduction, so the number of genes and their phenotypic effect (height and position of the Gaussian envelope) are free to evolve. The surface under the curve is fixed to prevent ‘supergenes’ from occurring. Change of the environmental conditions is simulated as a bounded random walk with a varying length of the step (a parameter representing variability of the environment). Using this approach, the model is able to reproduce the phenomenon of genome streamlining in more stable environments (analogical to e.g. oligotrophic gyre regions of the ocean) and genome complexification in variable environments. Horizontal gene transfer (HGT) was also introduced, but was found to act in a similar manner as gene duplication and shown no important contribution to the speed of evolution and the adaptive potential of the population

Implementing von Neumann’s architecture for machine self reproduction within the tierra artificial life platform to investigate evolvable genotype-phenotype mappings

John von Neumann first presented his theory of machine self reproduction in the late 1940's in which he described a machine capable of performing the logical steps necessary to accommodate self reproduction, and provided an explanation in principle for how arbitrarily complex machines can construct other (offspring) machines of equal or even greater complexity. In this thesis, a machine having the von Neumann architecture for self reproduction is designed to operate within the computational world of Tierra. This design implements a (mutable) genotype-phenotype mapping during reproduction, and acts as an exploratory model to observe the phenomena which may arise with such a system. A substitution mapping was chosen to carry out the genotype-phenotype mapping, and two specific implementations of a substitution mapping were investigated, via the use of a look-up table and a translation table. During implementation of the look-up table, preliminary experiments showed a degeneration to self copiers where a lineage of von Neumann style self reproducers degenerated into self copiers. Further experiments showed that a particular phenomenon emerges, where "pathological constructors" quickly develop, which can ultimately lead to total ecosystem collapse. If redundancy is introduced to the genotype-phenotype mapping, certain inheritable perturbations (mutations) prove to be non-reversible via a change to the genotype, which leads to a bias in the evolution of the genotype-phenotype mapping, consistently resulting in the loss of any target symbols from the mapping which are not vital for reproduction. It demonstrated how instances of Lamarkian inheritance may occur, which allowed these genetically ``non-reversible'' perturbations to be reversed, but only when accompanied by a very specific perturbation to the phenotype. The underlying dynamics of the chosen coding system was studied in order to better understand why these phenomena occur. When implementing a translation table, the space of possible mutations to the genotype-phenotype mapping was investigated and the same phenomena observed, where non vital symbols were lost from the mapping, and an instance of Lamarkian inheritance is necessary in order to introduce symbols to the mapping