Evolution as Computation by Laura F Landweber
The study of the genetic basis for evolution has flourished in this century, as well as our understanding of the evolvability and programmability of biological systems. Genetic algorithms meanwhile grew out of the realization that a computer program could use the biologically-inspired processes of mutation, recombination, and selection to solve hard optimization problems. Genetic and evolutionary programming provide further approaches to a wide variety of computational problems. A synthesis of these experiences reveals fundamental insights into both the computational nature of biological evolution and processes of importance to computer science. Topics include biological models of nucleic acid information processing and genome evolution; molecules, cells, and metabolic circuits that compute logical relationships; the origin and evolution of the genetic code; and the interface with genetic algorithms, genetic and evolutionary programming. This research combines theory and experiments to understand the computations that take place in cells and the combinatorial processes that drive evolution at the molecular level.-
Robot Evolution
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An Introduction to Metaheuristics for Optimization
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Deep Neural Evolution
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Lectures on Intelligent Systems
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Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms
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Coevolutionary Computation and Its Applications
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Visions of DNA Nanotechnology at 40 for the Next 40
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Cartesian Genetic Programming
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Bioinspired Computation in Combinatorial Optimization
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Self-organising Software
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Swarm Intelligence
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Algorithmic Bioprocesses
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General-Purpose Optimization Through Information Maximization
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Nature Inspired Optimisation for Delivery Problems
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Experimental Research in Evolutionary Computation
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Hyper-Heuristics: Theory and Applications
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Modelling in Molecular Biology
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Advances in Metaheuristics for Hard Optimization
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Foraging-Inspired Optimisation Algorithms
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Mobility in Process Calculi and Natural Computing
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Automating the Design of Data Mining Algorithms
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Contemporary Evolution Strategies
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Theory and Principled Methods for the Design of Metaheuristics
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Discrete and Topological Models in Molecular Biology
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Massively Parallel Evolutionary Computation on GPGPUs
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Multimodal Optimization by Means of Evolutionary Algorithms
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Computation in Living Cells
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Sensitivity Analysis for Neural Networks
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Reservoir Computing
From the reviews:
"Most algorithms used within the domain of so-called ‘soft-computing’ were directly inspired by our knowledge of biological ‘computation’ in living organisms… This book … as such, reflects the present state of the art. … The reading of the individual contributions should be very useful, both for the mathematician and the computer scientist, and is highly recommended since it erodes the trust in old and consolidated conceptions." (E. Pessa, Mathematical Reviews, 2004 i)
Laura F. Landweber is Assistant Professor of Biology at Princeton University and is the co-editor of DNA Based Computers II (AMS) and Evolution as Computation (forthcoming). Andrew P. Dobson is Associate Professor of Biology at Princeton University and is the author of Conservation and Biodiversity.
| SKU | Unavailable |
| ISBN 13 | 9783540667094 |
| ISBN 10 | 3540667091 |
| Title | Evolution as Computation |
| Author | Laura F Landweber |
| Series | Natural Computing Series |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer |
| Year published | 2002-11-27 |
| Number of pages | 333 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |