10/23/2004 - The fourth chapter of the book GENE EXPRESSION PROGRAMMING: MATHEMATICAL MODELING BY AN ARTIFICIAL INTELLIGENCE is now available to browse online at:

https://www.gene-expression-programming.com/Books/index.asp

The book has 7 chapters and the first four are now available (1 Introduction; 2 The Entities of Gene Expression Programming; 3 The Basic Gene Expression Algorithm; and 4 The Basic GEA in Problem Solving). The remaining chapters (5 Design of Neural Networks; 6 Combinatorial Optimization; and 7 Evolutionary Studies) will follow shortly.

Entries for Chapter 4:

Chapter 4: THE BASIC GEA IN PROBLEM SOLVING
1. Symbolic regression
1.1. Function finding on a one-dimensional parameter space
1.2. Function finding on a five-dimensional parameter space
1.3. Mining meaningful information from noisy data
2. Symbolic regression and the creation of numerical constants
2.1. Manipulation of numerical constants in GEP
2.2. Two approaches to the problem of constant creation
2.2.1. Direct manipulation of numerical constants
2.2.2. Creation of numerical constants from scratch
3. Parameter optimization
3.1. Multigenic chromosomes and multidimensional parameter optimization
3.2. Maximum seeking with GEP
4. Time series prediction
4.1. Evolution of Kolmogorov-Gabor polynomials
4.2. Simulating STROGANOFF and enhanced STROGANOFF with GEP
4.3. Predicting sunspots with GEP
5. Classification problems
5.1. Diagnosis of breast cancer
5.2. Credit screening
5.3. Fisher’s irises
6. Logic synthesis
6.1. Finding solutions to odd-parity functions with the basic gene expression algorithm
6.2. Finding solutions to odd-parity functions with UDFs
6.3. Finding solutions to odd-parity functions with ADFs
7. Evolving cellular automata rules for the density-classification problem
7.1. The density-classification task
7.2. Two new rules discovered by GEP