Evolution and Biocomputation [electronic resource] : Computational Models of Evolution / edited by Wolfgang Banzhaf, Frank H. Eeckman.Material type: TextLanguage: English Series: Lecture Notes in Computer Science: 899Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 1995Description: VIII, 284 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540491767Subject(s): Computer science | Computer software | Artificial intelligence | Cytology | Biology -- Mathematics | Combinatorics | Statistics | Computer Science | Algorithm Analysis and Problem Complexity | Artificial Intelligence (incl. Robotics) | Combinatorics | Mathematical Biology in General | Statistics for Life Sciences, Medicine, Health Sciences | Cell BiologyAdditional physical formats: Printed edition:: No titleDDC classification: 005.1 LOC classification: QA76.9.A43Online resources: Click here to access online
Editors' introduction -- Aspects of optimality behavior in population genetics theory -- Optimization as a technique for studying population genetics equations -- Emergence of mutualism -- Three illustrations of artificial life's working hypothesis -- Self-organizing algorithms derived from RNA interactions -- Modeling the connection between development and evolution: Preliminary report -- Soft genetic operators in Evolutionary Algorithms -- Analysis of selection, mutation and recombination in genetic algorithms -- The role of mate choice in biocomputation: Sexual selection as a process of search, optimization, and diversification -- Genome growth and the evolution of the genotype-phenotype map.
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.