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Biological computation

Author: Ehud Lamm; Ron Unger
Publisher: Boca Raton : CRC Press, ©2011.
Series: Chapman and Hall/CRC mathematical & computational biology series.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

Suitable for advanced undergraduates in computer science programs, this title covers major themes of bio-inspired computing, including cellular automata, molecular computation, genetic algorithms,  Read more...

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Document Type: Book
All Authors / Contributors: Ehud Lamm; Ron Unger
ISBN: 9781420087956 1420087959
OCLC Number: 650504310
Description: xix, 323 pages : illustrations ; 25 cm.
Contents: Chapter 1: Introduction and biological background --
1.1 Biological computation --
1.2 The influence of biology on mathematics --
historical examples --
1.3 Biological introduction --
1.3.1 The cell and its activities --
1.3.2 The structure of DNA --
1.3.3 The genetic code --
1.3.4 Protein synthesis and gene regulation --
1.3.5 Reproduction and heredity --
1.4 Models and simulations --
1.5 Summary --
1.6 Further reading --
1.7 Exercises --
1.7.1 Biological computation --
1.7.2 History --
1.7.3 Biological introduction --
1.7.4 Models and simulations --
1.8 Answers to selected exercises --
Chapter 2: Cellular automata --
2.1 Biological background --
2.1.1 Bacteria basics --
2.1.2 Genetic inheritance --
downward and sideways --
2.1.3 Diversity and the species question --
2.1.4 Bacteria and humans --
2.1.5 The sociobiology of bacteria --
2.2 The "game of life" --
2.3 General definition of cellular automata --
2.4 1-dimensional automata --
2.5 Examples of cellular automata --
2.5.1 Fur color --
2.5.2 Ecological models --
2.5.3 Food chain --
2.6 Comparison with a continuous mathematical model --
2.7 Computational universality --
2.7.1 What is universality? --
2.7.2 Cellular automata as a computational model --
2.7.3 How to prove that a CA is universal --
2.7.4 Universality of a two-dimensional cellular automation --
proof sketch --
2.7.5 Universality of the "game of life" --
proof sketch --
2.8 Self-replication --
2.9 Summary --
2.10 Pseudo-code --
2.11 Further reading --
2.12 Exercises --
2.12.1 "Game of life" --
2.12.2 Cellular automata --
2.12.3 Computing using cellular automata --
2.12.4 Self-replication --
2.12.5 Programming exercises. 2.13 Answers to selected exercises --
Chapter 3: Evolutionary computation --
3.1 Evolutionary biology and evolutionary computation --
3.1.1 Natural selection --
3.1.2 Evolutionary computation --
3.2 Genetic algorithms --
3.2.1 Selections and fitness --
3.2.2 Variations on fitness functions --
3.2.3 Genetic operators and the representation of solutions --
3.3 Example applications --
3.3.1 Scheduling --
3.3.2 Engineering optimization --
3.3.3 Pattern recognition and classification --
3.3.4 Designing cellular automata --
3.3.5 Designing neural networks --
3.3.6 Bioinformatics --
3.4 Analysis of the behavior of genetic algorithms --
3.4.1 Holland's building blocks hypothesis --
3.4.2 The schema theorem --
3.4.3 Corollaries of the schema theorem --
3.5 Lamarckian evolution --
3.6 Genetic programming --
3.7 A second look at the evolutionary process --
3.7.1 Mechanisms for the generation and inheritance of variations --
3.7.2 Selection --
3.8 Summary --
3.9 Pseudo-code --
3.10 Further reading --
3.11 Exercises --
3.11.1 Evolutionary computation --
3.11.2 Genetic algorithms --
3.11.3 Selection and fitness --
3.11.4 Genetic operators and the representation of solutions --
3.11.5 Analysis of the behavior of genetic algorithms --
3.11.6 Genetic programming --
3.11.7 Programming exercises --
3.12 Answers to selected exercises --
Chapter 4: Artificial neural networks --
4.1 Biological background --
4.1.1 Neural networks as computational model --
4.2 Learning --
4.3 Artificial neural networks --
4.3.1 General structure of artificial neural networks --
4.3.2 Training an artificial neural network --
4.4 The perceptron --
4.4.1 Definition of a perceptron --
4.4.2 Formal description of the behavior of a perceptron --
4.4.3 The perceptron learning rule --
4.4.4 Proving the convergence of the perceptron learning algorithm --
4.5 Learning in a multilayered network --
4.5.1 The backpropagation algorithm --
4.5.2 Analysis of learning algorithms --
4.5.3 Network design --
4.5.4 Examples of applications --
4.6 Associative memory --
4.6.1 Biological memory --
4.6.2 Hopfield networks. 4.6.3 Memorization in a Hopfield network --
4.6.4 Data retrieval in a Hopfield network --
4.6.5 The convergence of the process of updating the neurons --
4.6.6 Analyzing the capacity of a Hopfield network --
4.6.7 Application of a Hopfield network --
4.7 Unsupervised learning --
4.7.1 Self-organizing maps --
4.7.2 WEBSOM: example of using SOMs for document text mining --
4.8 Summary --
4.9 Further reading --
4.10 Exercises --
4.10.1 Single-layer perceptrons --
4.10.2 Multilayer networks --
4.10.3 Hopfield networks --
4.10.4 Self-organizing maps --
4.10.5 Summary --
4.11 Answers to selected exercises --
Chapter 5: Molecular computation --
5.1 Biological background --
5.1.1 PCR : Polymerase chain reaction --
5.1.2 Gel electrophoresis --
5.1.3 Restriction enzymes --
5.1.4 Ligation --
5.2 Computation using DNA --
5.2.1 Hamiltonian paths --
5.2.2 Solving SAT --
5.2.3 DNA tiling --
5.2.4 DNA computing --
summary --
5.3 Enzymatic computation --
5.3.1 Finite automata --
5.3.2 Enzymatic implementation of finite automata --
5.4 Summary --
5.5 Further reading --
5.6 Exercises --
5.6.1 Biological background --
5.6.2 Computing with DNA --
5.6.3 Enzymatic computation --
5.7 Answers to selected exercises --
Chapter 6: The never-ending story : additional topics at the interface between biology and computation --
6.1 Swarm intelligence --
6.1.1 Ant colony optimization algorithms --
6.1.2 Cemetery organization, larval sorting, and clustering --
6.1.3 Particle swarm optimization --
6.2 Artificial immune systems --
6.2.1 Identifying intrusions in a computer network --
6.3 Artificial life --
6.3.1 Avida --
6.3.2 Evolvable virtual creatures --
6.4 Systems biology --
6.4.1 Evolution of modularity --
6.4.2 Robustness of biological systems --
6.4.3 Formal languages for describing biological systems --
6.5 Summary --
6.6 Recommendations for additional reading --
6.6.1 Biological introduction. 6.6.2 Personal perspectives --
6.6.3 Modeling biological systems --
6.6.4 Biological computation --
6.6.5 Cellular automata --
6.6.6 Evolutionary computation --
6.6.7 Neural networks --
6.6.8 Molecular computation --
6.6.9 Swarm intelligence --
6.6.10 Systems biology --
6.6.11 Bioinformatics --
6.7 Further reading --
6.8 Exercises --
6.8.1 Swarm intelligence --
6.8.2 Artificial immune systems --
6.8.3 Artificial life --
6.8.4 Systems biology --
6.8.5 Programming exercises --
6.9 Answers to selected exercises.
Series Title: Chapman and Hall/CRC mathematical & computational biology series.
Responsibility: Ehud Lamm, Ron Unger.

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Biological computing, the three-billion-year-old goldmine of information processing concepts, is ready for our educational mainstream. This beautiful undergraduate text by Lamm and Unger may be the Read more...

 
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