Cooperation in self-organized heterogeneous swarms

Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems

Modelling the dynamics of genetic algorithms using statistical mechanics

A formalism for modelling the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics, originally due to Prugel-Bennett and Shapiro, is reviewed, generalized and improved upon. This formalism can be used to predict the averaged trajectory of macroscopic statistics describing the GA's population. These macroscopics are chosen to average well between runs, so that fluctuations from mean behaviour can often be neglected. Where necessary, non-trivial terms are determined by assuming maximum entropy with constraints on known macroscopics. Problems of realistic size are described in compact form and finite population effects are included, often proving to be of fundamental importance. The macroscopics used here are cumulants of an appropriate quantity within the population and the mean correlation (Hamming distance) within the population. Including the correlation as an explicit macroscopic provides a significant improvement over the original formulation. The formalism is applied to a number of simple optimization problems in order to determine its predictive power and to gain insight into GA dynamics. Problems which are most amenable to analysis come from the class where alleles within the genotype contribute additively to the phenotype. This class can be treated with some generality, including problems with inhomogeneous contributions from each site, non-linear or noisy fitness measures, simple diploid representations and temporally varying fitness. The results can also be applied to a simple learning problem, generalization in a binary perceptron, and a limit is identified for which the optimal training batch size can be determined for this problem. The theory is compared to averaged results from a real GA in each case, showing excellent agreement if the maximum entropy principle holds. Some situations where this approximation brakes down are identified. In order to fully test the formalism, an attempt is made on the strong sc np-hard problem of storing random patterns in a binary perceptron. Here, the relationship between the genotype and phenotype (training error) is strongly non-linear. Mutation is modelled under the assumption that perceptron configurations are typical of perceptrons with a given training error. Unfortunately, this assumption does not provide a good approximation in general. It is conjectured that perceptron configurations would have to be constrained by other statistics in order to accurately model mutation for this problem. Issues arising from this study are discussed in conclusion and some possible areas of further research are outlined

Aneuploidy in Health and Disease

Aneuploidy means any karyotype that is not euploid, anything that stands outside the norm. Two particular characteristics make the research of aneuploidy challenging. First, it is often hard to distinguish what is a cause and what is a consequence. Secondly, aneuploidy is often associated with a persistent defect in maintenance of genome stability. Thus, working with aneuploid, unstable cells means analyzing an ever changing creature and capturing the features that persist. In the book Aneuploidy in Health and Disease we summarize the recent advances in understanding the causes and consequences of aneuploidy and its link to human pathologies

Genomics of sexual and asexual reproduction in "Daphnia magna"

During my Ph.D., I used the next generation sequencing technology to investigate patterns of recombination and the genetic consequences of different reproductive modes of Daphnia magna. More precisely, I have used Restriction site Associated (RAD) sequencing to construct a high-density genetic map that can be coupled with the draft genome assembly of D. magna, thus, providing an essential tool for genome investigations in this widely used model organism (Chapter I). Such a map has enabled characterization of variation in the meiotic recombination rates across the genome of D. magna for the first time. Since recombination rates are an important parameter in almost any type of genetic research, this newly gained insight into the recombination landscape of D. magna offers a fundamental information for future studies of genome evolution, identification of genes underlying phenotypic traits and population genetic analyses. In addition to sexual reproduction, D. magna can also reproduce asexually to generate clutches of clonal offspring (ameiotic parthenogenesis). This feature of Daphnia biology is extremely useful for scientific experimentation where the genetic variation among tested individuals has to be minimized. However, over the last decade, reports of genome homogenization (loss of heterozygosity - LOH) in asexual lineages of D. pulex have indicated that asexual genomes are not static as it was previously assumed and that some levels of ameiotic recombination, in addition to mutation, may induce genetic variation among putative clones. However, comparing parthenogenetic offspring with their mothers at several thousand genetic markers generated by RAD-sequencing, I was not able to detect any LOH events in D. magna (Chapter II). I cannot exclude the possibility that ameiotic recombination indeed occurs in D. magna, however, my results indicate that such phenomenon is extremely rare or restricted to the very short genomic regions that I was unable to investigate, despite a high-density of markers used in this study. Nevertheless, the implementation of RAD-sequencing protocol for the genome studies of D. magna still enables interrogation of the transmission of genetic information from parents to offspring at unprecedented resolution. For an example, a RAD-sequencing based analysis of reduction in parental heterozygosity among rare ephippial hatchlings (typically produced by sexual reproduction) found in non-male producing populations of D. magna, has enabled differentiation between self-fertilization and automixis (meiotic parthenogenesis), by uncovering the subtle differences in genetic consequences of these reproductive strategies (Chapter III). Harnessing the ability of high-resolution genetic analysis it was demonstrated that, in the absence of males, D. magna can produce diapause eggs by automixis, and an additional type of asexual reproduction that was not previously reported for this species. Finally, RAD-sequencing European populations of D. magna revealed an association of genetic variation with the geographic location of individual samples (Chapter IV), a task which was not previously amenable using mitochondrial or microsatellite markers. This study provided a better insight into population genetic structure of D. magna and suggested that genetic differentiation is mainly driven by geographic distance. These results set a foundation for forthcoming studies aiming to disentangle past and future evolutionary processes shaping populations of this intriguing model organism. Taken together, research presented in my thesis illustrates the practicality of reduced representation genome sequencing for tackling diverse topics in evolutionary biology. By increasing awareness of non-randomness of meiotic recombination across the genome of D. magna, the diversity of reproductive mechanisms it can employ, and its large-scale population structure, I hope this work will contribute to further understanding of the remarkable adaptive capacity this crustacean is famous for

A Causal Interpretation of Selection Theory

The following dissertation is an inferentialist account of classical population genetics. I present the theory as a definite body of interconnected inferential rules for generating mathematical models of population dynamics. To state those rules, I use the notion of causation as a primitive. First, I put forward a rule stating the circumstances of application of the theory, one that uses causal language to pick out the types of entities over which the theory may be deployed. Next, I offer a rule for grouping such entities into populations based on their competitive causal relationships. Then I offer a general algorithm for generating classical population genetics models for such populations on the basis of what causal influences operate within them.Dynamical models in population genetics are designed to demystify natural phenomena, chiefly to show how adaptation, altruism, and genetic polymorphism can be explained in terms of natural rather than supernatural processes. In order for the theory to serve this purpose, it must be possible to understand, in a principled fashion, when and how to deploy the theory. By presenting the theory as a system of ordered inferential rules that takes causal information as its critical input and yields dynamical models as its outputs, I show explicitly how classical population genetics functions as a non-circular theoretical apparatus for generating explanations. The generalization of the theory achieved by presenting it using causal vocabulary shows how the scope of the theory of natural selection extends beyond its traditional domain of systems distinguished by genetic variations

Compact Dynamic Optimisation Algorithm

In recent years, the field of evolutionary dynamic optimisation has seen significant increase in scientific developments and contributions. This is as a result of its relevance in solving academic and real-world problems. Several techniques such as hyper-mutation, hyper-learning, hyper-selection, change detection and many more have been developed specifically for solving dynamic optimisation problems. However, the complex structure of algorithms employing these techniques make them unsuitable for real-world, real-time dynamic optimisation problem using embedded systems with limited memory. The work presented in this thesis focuses on a compact approach as an alternative to population based optimisation algorithm, suitable for solving real-time dynamic optimisation problems. Specifically, a novel compact dynamic optimisation algorithm suitable for embedded systems with limited memory is presented. Three novel dynamic approaches that augment and enhance the evolving properties of the compact genetic algorithm in dynamic environments are introduced. These are 1.) change detection scheme that measures the degree of dynamic change 2.) mutation schemes whereby the mutation rates is directly linked to the detected degree of change and 3.) change trend scheme the monitors change pattern exhibited by the system. The novel compact dynamic optimization algorithm outlined was applied to two differing dynamic optimization problems. This work evaluates the algorithm in the context of tuning a controller for a physical target system in a dynamic environment and solving a dynamic optimization problem using an artificial dynamic environment generator. The novel compact dynamic optimisation algorithm was compared to some existing dynamic optimisation techniques. Through a series of experiments, it was shown that maintaining diversity at a population level is more efficient than diversity at an individual level. Among the five variants of the novel compact dynamic optimization algorithm, the third variant showed the best performance in terms of response to dynamic changes and solution quality. Furthermore, it was demonstrated that information transfer based on dynamic change patterns can effectively minimize the exploration/exploitation dilemma in a dynamic environment

The role of visual adaptation in cichlid fish speciation

D. Shane Wright (1) , Ole Seehausen (2), Ton G.G. Groothuis (1), Martine E. Maan (1) (1) University of Groningen; GELIFES; EGDB(2) Department of Fish Ecology & Evolution, EAWAG Centre for Ecology, Evolution and Biogeochemistry, Kastanienbaum AND Institute of Ecology and Evolution, Aquatic Ecology, University of Bern.In less than 15,000 years, Lake Victoria cichlid fishes have radiated into as many as 500 different species. Ecological and sexual sel ection are thought to contribute to this ongoing speciation process, but genetic differentiation remains low. However, recent work in visual pigment genes, opsins, has shown more diversity. Unlike neighboring Lakes Malawi and Tanganyika, Lake Victoria is highly turbid, resulting in a long wavelength shift in the light spectrum with increasing depth, providing an environmental gradient for exploring divergent coevolution in sensory systems and colour signals via sensory drive. Pundamilia pundamila and Pundamilia nyererei are two sympatric species found at rocky islands across southern portions of Lake Victoria, differing in male colouration and the depth they reside. Previous work has shown species differentiation in colour discrimination, corresponding to divergent female preferences for conspecific male colouration. A mechanistic link between colour vision and preference would provide a rapid route to reproductive isolation between divergently adapting populations. This link is tested by experimental manip ulation of colour vision - raising both species and their hybrids under light conditions mimicking shallow and deep habitats. We quantify the expression of retinal opsins and test behaviours important for speciation: mate choice, habitat preference, and fo raging performance