Buy the Book

  GEP Biblio

  Visit Gepsoft


© C. FERREIRA, 2002 (Terms of Use) ISBN: 9729589054

Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence

Table of Contents



1. Introduction
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
2.1.4. Structural and functional diversity of chromosomes
2.2. Expression trees and the phenotype
2.2.1. Information decoding: Translation
2.2.2. Posttranslational interactions and linking functions
2.2.3. Cells and the evolution of linking functions
2.2.4. Other levels of complexity
2.2.5. 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 selection
3.2.1. Fitness functions and the selection environment
3.2.2. Selection
3.3. Reproduction with modification
3.3.1. Replication and selection
3.3.2. Mutation
3.3.3. Transposition and insertion sequence elements Transposition of insertion sequence elements Root transposition Gene transposition
3.3.4. 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. Symbolic regression and the creation of numerical constants
4.2.1. Manipulation of numerical constants in GEP
4.2.2. Two approaches to the problem of constant creation Direct manipulation of numerical constants Creation of numerical constants from scratch
4.3. Parameter optimization
4.3.1. Multigenic chromosomes and multidimensional parameter optimization
4.3.2. Maximum seeking with GEP
4.4. Time series prediction
4.4.1. Evolution of Kolmogorov-Gabor polynomials
4.4.2. Simulating STROGANOFF and enhanced STROGANOFF with GEP
4.4.3. Predicting sunspots with GEP
4.5. Classification problems
4.5.1. Diagnosis of breast cancer
4.5.2. Credit screening
4.5.3. Fisher’s irises
4.6. Logic synthesis
4.6.1. Finding solutions to odd-parity functions with the basic gene expression algorithm
4.6.2. Finding solutions to odd-parity functions with UDFs
4.6.3. Finding solutions to odd-parity functions with ADFs
4.7. Evolving cellular automata rules for the density-classification problem
4.7.1. The density-classification task
4.7.2. Two new rules discovered by GEP

5. Design of neural networks
5.1. Genes with multiple domains for neural network simulation
5.2. Special search operators
5.2.1. Domain specific transposition
5.2.2. Intragenic two-point recombination
5.2.3. Direct mutation of weights and thresholds
5.3. Solving problems with GEP neural networks
5.3.1. Neural network for the exclusive-or problem
5.3.2. Neural network for the 6-multiplexer
5.4. Evolutionary dynamics of GEP-nets

6. Combinatorial optimization
6.1. Multigene families and scheduling problems
6.2. Combinatorial-specific operators: Performance and mechanisms
6.2.1. Inversion
6.2.2. Gene deletion/insertion
6.2.3. Restricted permutation
6.2.4. Other search operators Sequence deletion/insertion Generalized permutation
6.3. Two scheduling problems
6.3.1. The traveling salesperson problem
6.3.2. The task assignment problem
6.4. Evolutionary dynamics of simple GEP systems

7. Evolutionary studies
7.1. Genetic operators and their power
7.1.1. Comparing the performance of mutation, transposition, and recombination
7.1.2. Evolutionary dynamics of different types of GEP populations Mutation Transposition Recombination
7.2. The founder effect
7.2.1. Choosing non-homogenizing and homogenizing populations to study the founder effect
7.2.2. Analyzing the founder effect in simulated evolutionary processes
7.3. Testing the building block hypothesis
7.4. The role of neutrality in evolution
7.4.1. Genetic neutrality in unigenic systems
7.4.2. Genetic neutrality in multigenic systems
7.5. The higher hierarchical organization of multigenic systems
7.6. The open-ended evolution of GEP populations
7.7. Analysis of different selection schemes


Home | Next