477 research outputs found
Artificial evolution with Binary Decision Diagrams: a study in evolvability in neutral spaces
This thesis develops a new approach to evolving Binary Decision Diagrams, and uses it to study evolvability issues. For reasons that are not yet fully understood, current approaches to artificial evolution fail to exhibit the evolvability so readily exhibited in nature. To be able to apply evolvability to artificial evolution the field must first understand and characterise it; this will then lead to systems which are much more capable than they are currently. An experimental approach is taken. Carefully crafted, controlled experiments elucidate the mechanisms and properties that facilitate evolvability, focusing on the roles and interplay between neutrality, modularity, gradualism, robustness and diversity. Evolvability is found to emerge under gradual evolution as a biased distribution of functionality within the genotype-phenotype map, which serves to direct phenotypic variation. Neutrality facilitates fitness-conserving exploration, completely alleviating local optima. Population diversity, in conjunction with neutrality, is shown to facilitate the evolution of evolvability. The search is robust, scalable, and insensitive to the absence of initial diversity. The thesis concludes that gradual evolution in a search space that is free of local optima by way of neutrality can be a viable alternative to problematic evolution on multi-modal landscapes
Towards an Information Theoretic Framework for Evolutionary Learning
The vital essence of evolutionary learning consists of information flows between the environment and the entities differentially surviving and reproducing therein. Gain or loss of information in individuals and populations due to evolutionary steps should be considered in evolutionary algorithm theory and practice. Information theory has rarely been applied to evolutionary computation - a lacuna that this dissertation addresses, with an emphasis on objectively and explicitly evaluating the ensemble models implicit in evolutionary learning. Information theoretic functionals can provide objective, justifiable, general, computable, commensurate measures of fitness and diversity.
We identify information transmission channels implicit in evolutionary learning. We define information distance metrics and indices for ensembles. We extend Price\u27s Theorem to non-random mating, give it an effective fitness interpretation and decompose it to show the key factors influencing heritability and evolvability. We argue that heritability and evolvability of our information theoretic indicators are high. We illustrate use of our indices for reproductive and survival selection. We develop algorithms to estimate information theoretic quantities on mixed continuous and discrete data via the empirical copula and information dimension. We extend statistical resampling. We present experimental and real world application results: chaotic time series prediction; parity; complex continuous functions; industrial process control; and small sample social science data. We formalize conjectures regarding evolutionary learning and information geometry
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Adaptive evolution in static and dynamic environments
This thesis provides a framework for describing a canonical evolutionary system. Populations of individuals are envisaged as traversing a search space structured by genetic and developmental operators under the influence of selection. Selection acts on individuals' phenotypic expressions, guiding the population over an evaluation landscape, which describes an idealised evaluation surface over the phenotypic space. The corresponding valuation landscape describes evaluations over the genotypic space and may be transformed by within generation adaptive (learning) or maladaptive (fault induction) local search.
Populations subjected to particular genetic and selection operators are claimed to evolve towards a region of the valuation landscape with a characteristic local ruggedness, as given by the runtime operator correlation coefficient. This corresponds to the view of evolution discovering an evolutionarily stable population, or quasi-species, held in a state of dynamic equilibrium by the operator set and evaluation function. This is demonstrated by genetic algorithm experiments using the NK landscapes and a novel, evolvable evaluation function, The Tower of Babel. In fluctuating environments of varying temporal ruggedness, different operator sets are correspondingly more or less adapted.
Quantitative genetics analyses of populations in sinusoidally fluctuating conditions are shown to describe certain well known electronic filters. This observation suggests the notion of Evolutionary Signal Processing. Genetic algorithm experiments in which a population tracks a sinusoidally fluctuating optimum support this view. Using a self-adaptive mutation rate, it is possible to tune the evolutionary filter to the environmental frequency. For a time varying frequency, the mutation rate reacts accordingly. With local search, the valuation landscape is transformed through temporal smoothing. By coevolving modifier genes for individual learning and the rate at which the benefits may be directly transmitted to the next generation, the relative adaptedness of individual learning and cultural inheritance according to the rate of environmental change is demonstrated
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Deviation from panmixia via assortative mating and divergent habitat preferences
The speciation process is often viewed to start from panmictic populations. Understanding the evolutionary mechanisms that cause populations to deviate from panmixia is essential to understanding the initial stage of population divergence that may lead to speciation. My dissertation focuses on the evolution of two mechanisms that cause deviation from panmixia: assortative mating and divergent habitat preferences. The first chapter is a meta-analysis on published measures of the strength of assortative mating within natural animal populations. Results showed that deviation from panmixia via weak positive assortative mating was typical within natural animal populations, while disassortative mating was rare or absent. Results also suggested that assortative mating did not typically evolve adaptively, but instead as an incidental consequence of other mechanisms, such as spatial segregation. Divergent habitat uses are important drivers of spatial segregation. The second chapter revealed a behavioral mechanism of divergent habitat uses between parapatric lake and stream threespine stickleback populations. The results showed strong divergent rheotaxis between lake and stream fish during their breeding season. The divergence is likely to contribute to the sorting of lake and stream fish into their natal habitats and promote habitat-based assortative mating. The third chapter focused on the neuroanatomical and morphological mechanisms of rheotaxis. Results showed significant correlations between the numbers of neuromasts (functional units of the lateral line) and rheotaxis in both lab-reared and wild-caught threespine stickleback. Results also showed heritable divergence in lateral line structure between parapatric lake and stream stickleback, suggesting that divergent rheotaxis and the resulting divergent habitat uses are likely to have a heritable component. In summary, my dissertation revealed ultimate evolutionary mechanisms of assortative mating and proximate evolutionary mechanisms of divergent habitat uses. These results shed light on the understanding of the beginning of population divergence and ultimately speciation.Ecology, Evolution and Behavio
New genetic algorithms for constrained optimisation and applications to design of composite laminates
A general purpose constraint handling technique for genetic algorithms (GA) is developed by borrowing principles from multi-objective optimisation. This is in view of the many issues still facing constraint handling in GA, particularly in the number of control parameters that overwhelms the user, as well as other GA parameters, which are currently lacking in heuristics to guide successful implementations. Constraints may be handled as individual objectives of decreasing priorities or by a weighted-sum measurement of normalised violation, as would be done in multi-objective scenarios, with full consideration of the main cost function. Rather than the unnecessary specialisation seen in many new heuristics proposed for GA, the simplicity, generality and flexibility of the technique is maintained, where several options such as partial or full constraint evaluation, tangible or Pareto-ranked fitness, and implicit dominance evaluation are presented. By reducing the number of constraint evaluations, these options increase the probability of discovering optimal regions, and hence increase GA efficiency. Studies in applications to a constrained numerical problem, and to the design of realistic composite laminate plates and structures, serve to demonstrate the ease of implementation and general reliability in heavily constrained problems. The difference in the dynamics of partial or full violation knowledge showed that while the former reduced the overall number of constraint evaluations performed, the latter compromises for the expense of full constraint evaluations in the reduced number of GA generations, whether in terms of discovering feasible regions or optimal solutions. The benefit of partial or full constraint evaluations is subjective, as it ultimately depends on the trade-off in the computational cost of constraint evaluations and GA search.EThOS - Electronic Theses Online ServiceGBUnited Kingdo
Towards Structural Systems Pharmacology to Study Complex Diseases and Personalized Medicine
Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases
New genetic algorithms for constrained optimisation and applications to design of composite laminates
A general purpose constraint handling technique for genetic algorithms (GA) is developed by borrowing principles from multi-objective optimisation. This is in view of the many issues still facing constraint handling in GA, particularly in the number of control parameters that overwhelms the user, as well as other GA parameters, which are currently lacking in heuristics to guide successful implementations. Constraints may be handled as individual objectives of decreasing priorities or by a weighted-sum measurement of normalised violation, as would be done in multi-objective scenarios, with full consideration of the main cost function. Rather than the unnecessary specialisation seen in many new heuristics proposed for GA, the simplicity, generality and flexibility of the technique is maintained, where several options such as partial or full constraint evaluation, tangible or Pareto-ranked fitness, and implicit dominance evaluation are presented. By reducing the number of constraint evaluations, these options increase the probability of discovering optimal regions, and hence increase GA efficiency. Studies in applications to a constrained numerical problem, and to the design of realistic composite laminate plates and structures, serve to demonstrate the ease of implementation and general reliability in heavily constrained problems. The difference in the dynamics of partial or full violation knowledge showed that while the former reduced the overall number of constraint evaluations performed, the latter compromises for the expense of full constraint evaluations in the reduced number of GA generations, whether in terms of discovering feasible regions or optimal solutions. The benefit of partial or full constraint evaluations is subjective, as it ultimately depends on the trade-off in the computational cost of constraint evaluations and GA search
Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases
Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants.
Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets.
Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.Siirretty Doriast
White Paper 2: Origins, (Co)Evolution, Diversity & Synthesis Of Life
Publicado en Madrid, 185 p. ; 17 cm.How life appeared on Earth and how then it diversified into the different and currently existing forms of life are the unanswered questions that will be discussed this volume. These questions delve into the deep past of our planet, where biology intermingles with geology and chemistry, to explore the origin of life and understand its evolution, since “nothing makes sense in biology except in the light of evolution” (Dobzhansky, 1964). The eight challenges that compose this volume summarize our current knowledge and future research directions touching different aspects of the study of evolution, which can be considered a fundamental discipline of Life Science. The volume discusses recent theories on how the first molecules arouse, became organized and acquired their structure, enabling the first forms of life. It also attempts to explain how this life has changed over time, giving rise, from very similar molecular bases, to an immense biological diversity, and to understand what is the hylogenetic relationship among all the different life forms. The volume further analyzes human evolution, its relationship with the environment and its implications on human health and society. Closing the circle, the volume discusses the possibility of designing new biological machines, thus creating a cell prototype from its components and whether this knowledge can be applied to improve our ecosystem. With an effective coordination among its three main areas of knowledge, the CSIC can become an international benchmark for research in this field
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