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Biological Computation 1st Edition by Ehud Lamm, Ron Unger ISBN 1420087959 9781420087956

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Author(s): Ehud Lamm, Ron Unger
ISBN(s): 9781420087956, 1420087959
Edition: 1
File Details: PDF, 13.04 MB
Year: 2011
Language: english
SKU: EB-2500262 Category: Tags: ,

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ISBN 10: 1420087959 
ISBN 13: 9781420087956
Author: Ehud Lamm, Ron Unger

The area of biologically inspired computing, or biological computation, involves the development of new, biologically based techniques for solving difficult computational problems. A unified overview of computer science ideas inspired by biology, Biological Computation presents the most fundamental and significant concepts in this area. In the book, students discover that bacteria communicate, that DNA can be used for performing computations, how evolution solves optimization problems, that the way ants organize their nests can be applied to solve clustering problems, and what the human immune system can teach us about protecting computer networks. The authors discuss more biological examples such as these, along with the computational techniques developed from these scenarios. The text focuses on cellular automata, evolutionary computation, neural networks, and molecular computation. Each chapter explores the biological background, describes the computational techniques, gives examples of applications, discusses possible variants of the techniques, and includes exercises and solutions. The authors use the examples and exercises to illustrate key ideas and techniques. Clearly conveying the essence of the major computational approaches in the field, this book brings students to the point where they can either produce a working implementation of the techniques or effectively use one of the many available implementations. Moreover, the techniques discussed reflect fundamental principles that can be applied beyond bio-inspired computing. Supplementary material is available on Dr. Unger’s website.

Biological Computation 1st Table of 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 Automaton—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 Selection 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 Hollands 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
4.6.8 Further Uses of the 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

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Tags: Ehud Lamm, Ron Unger, Biological Computation