GEP Book: Table of Contents

Gene Expression Programming:
Mathematical Modeling by an Artificial Intelligence
Cāndida Ferreira




Hardcover: 478 pages
Publisher: Springer (May 2006), 2nd Edition
ISBN: 3540327967

Buy online from: or or Publisher

Table of Contents for the Second Edition
Sections and chapters with new material are marked in red.
Preface to the Second Edition
Preface to the First Edition
List of Symbols

1 Introduction: The Biological Perspective
1.1 The Entities of Biological Gene Expression
1.1.1 DNA
1.1.2 RNA
1.1.3 Proteins
1.2 Biological Gene Expression
1.2.1 Genome Replication
1.2.2 Genome Restructuring: Mutation, Recombination, Transposition, and Gene Duplication Mutation Recombination Transposition Gene duplication
1.2.3 Transcription
1.2.4 Translation and Posttranslational Modifications Translation Posttranslational Modifications
1.3 Adaptation and Evolution
1.4 Genetic Algorithms
1.5 Genetic Programming
1.6 Gene Expression Programming

2 The Entities of Gene Expression Programming
2.1 The Genome
2.1.1 Open Reading Frames and Genes
2.1.2 Structural and Functional Organization of Genes
2.1.3 Multigenic Chromosomes
2.2 Expression Trees and the Phenotype
2.2.1 Information Decoding: Translation
2.2.2 Posttranslational Interactions and Linking Functions
2.3 Cells and the Evolution of Linking Functions
2.3.1 Homeotic Genes and the Cellular System
2.3.2 Multicellular Systems with Multiple Main Programs

2.4 Other Levels of Complexity
2.5 Karva Language: The Language of GEP

3 The Basic Gene Expression Algorithm
3.1 Populations of Individuals
3.1.1 Creation of the Initial Population
3.1.2 Subsequent Generations and Elitism
3.2 Fitness Functions and the Selection Environment
3.2.1 The Selection Environment
3.2.2 Fitness Functions for Symbolic Regression Number of Hits Precision and Selection Range Mean Squared Error R-square
3.2.3 Fitness Functions for Classification and Logic Synthesis Number of Hits Hits with Penalty Sensitivity / Specificity Positive Predictive Value / Negative Predictive Value
3.2.4 Selection Mechanism
3.3 Reproduction with Modification
3.3.1 Replication and Selection
3.3.2 Mutation
3.3.3 Inversion
3.3.4 Transposition and Insertion Sequence Elements Transposition of IS Elements Root Transposition Gene Transposition
3.3.5 Recombination One-point Recombination Two-point Recombination Gene Recombination
3.4 Solving a Simple Problem with GEP

4 The Basic GEA in Problem Solving
4.1 Symbolic Regression
4.1.1 Function Finding on a One-dimensional Parameter Space
4.1.2 Function Finding on a Five-dimensional Parameter Space
4.1.3 Mining Meaningful Information from Noisy Data
4.2 Classification Problems
4.2.1 Diagnosis of Breast Cancer
4.2.2 Credit Screening
4.2.3 Fisher's Irises Decomposing a Three-class Problem Multiple Genes for Multiple Outputs
4.3 Logic Synthesis and Parsimonious Solutions
4.3.1 Fitness Functions with Parsimony Pressure
4.3.2 Universal Logical Systems Boolean Logic Nand Logic Nor Logic Reed-Muller Logic Mux Logic
4.3.3 Using User Defined Functions as Building Blocks Odd-parity Functions Exactly-one-on Functions

4.4 Evolving Cellular Automata Rules for the Density-classification Problem
4.4.1 The Density-classification Task
4.4.2 Two New Rules Discovered by GEP

5 Numerical Constants and the GEP-RNC Algorithm
5.1 Handling Constants in Automatic Programming
5.2 Genes with Multiple Domains to Encode RNCs
5.3 Multigenic Systems with RNCs
5.4 Special Search Operators for Fine-tuning the RNCs
5.4.1 Dc-specific Mutation
5.4.2 Dc-specific Inversion
5.4.3 Dc-specific Transposition
5.4.4 Direct Mutation of RNCs
5.5 Solving a Simple Problem with GEP-RNC
5.6 Three Approaches to the Creation of NCs
5.6.1 Problems and Settings
5.6.2 Sequence Induction
5.6.3 "V" Function
5.6.4 Diagnosis of Breast Cancer
5.6.5 Analog Circuit Design

6 Automatically Defined Functions in Problem Solving
6.1 Solving a Simple Modular Function with ADFs
6.2 Odd-parity Functions
6.3 Kepler's Third Law
6.4 RNCs and the Cellular System
6.4.1 Incorporating RNCs in ADFs
6.4.2 Designing Analog Circuits with the ADF-RNC Algorithm
6.5 Diagnosis of Breast Cancer
6.6 Multiple Cells for Multiple Outputs: The Iris Problem

7 Polynomial Induction and Time Series Prediction
7.1 Evolution of Kolmogorov-Gabor Polynomials
7.2 Simulating STROGANOFF in GEP
7.3 Evaluating the Performance of STROGANOFF
7.3.1 Original and Enhanced STROGANOFF
7.3.2 Simpler GEP Systems
7.4 Predicting Sunspots with GEP

8 Parameter Optimization
8.1 The HZero Algorithm
8.1.1 The Architecture
8.1.2 Optimization of a Simple Function
8.2 The GEP-PO Algorithm
8.2.1 The Architecture
8.2.2 Optimization of a Simple Function
8.3 Maximum Seeking with GEP

9 Decision Tree Induction
9.1 Decision Trees with Nominal Attributes
9.1.1 The Architecture
9.1.2 A Simple Problem with Nominal Attributes
9.2 Decision Trees with Numeric/Mixed Attributes
9.2.1 The Architecture
9.2.2 A Simple Problem with Mixed Attributes
9.3 Solving Problems with GEP Decision Trees
9.3.1 Diagnosis of Breast Cancer
9.3.2 Classification of Irises
9.3.3 The Lymphography Problem
9.3.4 The Postoperative Patient Problem
9.4 Pruning Trees with Parsimony Pressure

10 Design of Neural Networks
10.1 Genes with Multiple Domains for NN Simulation
10.2 Special Search Operators
10.2.1 Domain-specific Inversion
10.2.2 Domain-specific Transposition
10.2.3 Intragenic Two-point Recombination
10.2.4 Direct Mutation of Weights and Thresholds
10.3 Solving Problems with GEP Neural Networks
10.3.1 Neural Network for the Exclusive-or Problem
10.3.2 Neural Network for the 6-Multiplexer
10.4 Evolutionary Dynamics of GEP-Nets

11 Combinatorial Optimization
11.1 Multigene Families and Scheduling Problems
11.2 Combinatorial-specific Operators: Performance and Mechanisms
11.2.1 Inversion
11.2.2 Gene Deletion/Insertion
11.2.3 Restricted Permutation
11.2.4 Other Search Operators Sequence Deletion/Insertion Generalized Permutation
11.3 Two Scheduling Problems
11.3.1 The Traveling Salesperson Problem
11.3.2 The Task Assignment Problem
11.4 Evolutionary Dynamics of Simple GEP Systems

12 Evolutionary Studies
12.1 Genetic Operators and their Power
12.1.1 Their Performances
12.1.2 Evolutionary Dynamics of Different Types of Populations Mutation Transposition Recombination
12.2 The Founder Effect
12.2.1 Choosing the Population Types
12.2.2 The Founder Effect in Simulated Evolutionary Processes
12.3 Testing the Building Block Hypothesis
12.4 The Role of Neutrality in Evolution
12.4.1 Genetic Neutrality in Unigenic Systems
12.4.2 Genetic Neutrality in Multigenic Systems
12.5 The Higher Organization of Multigenic Systems
12.6 The Open-ended Evolution of GEP Populations
12.7 Analysis of Different Selection Schemes


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Last update: 23/July/2013
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