Computational Methods to Study Phenotype Evolution and Feature Selection Techniques for Biological Data Under Evolutionary Constraints - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Methods to Study Phenotype Evolution and Feature Selection Techniques for Biological Data Under Evolutionary Constraints write by Christina Kratsch. This book was released on 2014. Computational Methods to Study Phenotype Evolution and Feature Selection Techniques for Biological Data Under Evolutionary Constraints available in PDF, EPUB and Kindle.
Models and Methods for Biological Evolution
Models and Methods for Biological Evolution - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Models and Methods for Biological Evolution write by Gilles Didier. This book was released on 2024-05-21. Models and Methods for Biological Evolution available in PDF, EPUB and Kindle. Biological evolution is the phenomenon concerning how species are born, are transformed or disappear over time. Its study relies on sophisticated methods that involve both mathematical modeling of the biological processes at play and the design of efficient algorithms to fit these models to genetic and morphological data. Models and Methods for Biological Evolution outlines the main methods to study evolution and provides a broad overview illustrating the variety of formal approaches used, notably including combinatorial optimization, stochastic models and statistical inference techniques. Some of the most relevant applications of these methods are detailed, concerning, for example, the study of migratory events of ancient human populations or the progression of epidemics. This book should thus be of interest to applied mathematicians interested in central problems in biology, and to biologists eager to get a deeper understanding of widely used techniques of evolutionary data analysis.
Computational Methods to Investigate Connectivity in Evolvable Systems
Computational Methods to Investigate Connectivity in Evolvable Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Computational Methods to Investigate Connectivity in Evolvable Systems write by Acacia Lee Ackles. This book was released on 2022. Computational Methods to Investigate Connectivity in Evolvable Systems available in PDF, EPUB and Kindle. Evolution sheds light on all of biology, and evolutionary dynamics underlie some of the most pressing issues we face today. If we can deepen our understanding of evolution, we can better respond to these various challenges. However, studying such processes directly can be difficult; biological data is naturally messy, easily confounded, and often limited. Fortunately, we can use computational modeling to help simplify and systematically untangle complex evolutionary processes. The aim of this dissertation is therefore to develop innovative computational frameworks to describe, quantify, and build intuition about evolutionary phenomena, with a focus on connectivity within evolvable systems. Here I introduce three such computational frameworks which address the importance of connectivity in systems across scales.First, I introduce rank epistasis, a model of epistasis that does not rely on baseline assumptions of genetic interactions. Rank epistasis borrows rank-based comparison testing from parametric statistics to quantify mutational landscapes around a target locus and identify how much that landscape is perturbed by mutation at that locus. This model is able to correctly identify lack of epistasis where existing models fail, thereby providing better insight into connectivity at the genome level.Next, I describe the comparative hybrid method, an approach to piecewise study of complex phenotypes. This model creates hybridized structures of well-known cognitive substrates in order to address what facilitates the evolution of learning. The comparative hybrid model allowed us to identify both connectivity and discretization as important components to the evolution of cognition, as well as demonstrate how both these components interact in different cognitive structures. This approach highlights the importance of recognizing connected components at the level of the phenotype.Finally, I provide an engineering point of view for Tessevolve, a virtual reality enabled system for viewing fitness landscapes in multiple dimensions. While traditional methods have only allowed for 2D visualization, Tessevolve allows the user to view fitness landscapes scaled across 2D, 3D, and 4D. Visualizing these landscapes in multiple dimensions in an intuitive VR-based system allowed us to identify how landscape traversal changes as dimensions increase, demonstrating the way that connections between points across fitness landscapes are affected by dimensionality. As a whole, this dissertation looks at connectivity in computational structures across a broad range of biological scales. These methods and metrics therefore expand our computational toolkit for studying evolution in multiple systems of interest: genotypic, phenotypic, and at the whole landscape level.
Evolution and Biocomputation
Evolution and Biocomputation - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Evolution and Biocomputation write by Wolfgang Banzhaf. This book was released on 1995-03-06. Evolution and Biocomputation available in PDF, EPUB and Kindle. This volume comprises ten thoroughly refereed and revised full papers originating from an interdisciplinary workshop on biocomputation entitled "Evolution as a Computational Process", held in Monterey, California in July 1992. This book is devoted to viewing biological evolution as a giant computational process being carried out over a vast spatial and temporal scale. Computer scientists, mathematicians and physicists may learn about optimization from looking at natural evolution and biologists may learn about evolution from studying artificial life, game theory, and mathematical optimization. In addition to the ten full papers addressing e.g. population genetics, emergence, artificial life, self-organization, evolutionary algorithms, and selection, there is an introductory survey and a subject index.
Machine Learning for Evolution Strategies
Machine Learning for Evolution Strategies - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Machine Learning for Evolution Strategies write by Oliver Kramer. This book was released on 2016-05-25. Machine Learning for Evolution Strategies available in PDF, EPUB and Kindle. This book introduces numerous algorithmic hybridizations between both worlds that show how machine learning can improve and support evolution strategies. The set of methods comprises covariance matrix estimation, meta-modeling of fitness and constraint functions, dimensionality reduction for search and visualization of high-dimensional optimization processes, and clustering-based niching. After giving an introduction to evolution strategies and machine learning, the book builds the bridge between both worlds with an algorithmic and experimental perspective. Experiments mostly employ a (1+1)-ES and are implemented in Python using the machine learning library scikit-learn. The examples are conducted on typical benchmark problems illustrating algorithmic concepts and their experimental behavior. The book closes with a discussion of related lines of research.
