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Introduction

1. The APS Environment
1.1. Getting Started
1.1.1. Starting APS
1.1.2. Loading Data
1.1.3. Evolving a Model
1.1.4. Optimizing a Model Evolved by APS
1.1.5. Evaluating the Accuracy of a Model
1.1.6. Testing the Generalizing Capabilities of a Model
1.1.7. Generating Code Automatically
1.1.8. Visualizing Parse Trees
1.1.9. Scoring a Model
1.2. APS Tools
1.2.1. Function Finding Tools
1.2.2. Classification Tools
1.2.3. Time Series Analysis Tools
1.3. APS Panels
1.3.1. Report
1.3.2. Data
1.3.2.1. Training Data
1.3.2.2. Testing Data
1.3.3. Settings
1.3.3.1. General Settings
1.3.3.2. Fitness Function
1.3.3.3. Genetic Operators
1.3.3.4. Numerical Constants
1.3.4. Functions
1.3.4.1. Mathematical Functions
1.3.4.2. Static UDFs
1.3.5. Run
1.3.6. History
1.3.7. Results
1.3.7.1. Training Results
1.3.7.2. Testing Results
1.3.8. Predictions
1.3.9. Model
1.4. Evolving APS Models
1.4.1. Preparing and Analyzing Data
1.4.2. Choosing the Function Set
1.4.3. Choosing the Chromosome Architecture
1.4.4. Choosing the Population Size
1.4.5. Choosing the Degree of Genetic Modification
1.4.6. Choosing the Fitness Function
1.4.7. Creating the Model
1.4.8. Evaluating the Model
1.4.9. Modeling from Seed
1.5. Making Predictions with a Model
1.5.1. Scoring from Databases
1.5.2. Scoring from Text Files
1.5.3. Making Predictions with Time Series Models

2. Working with Function Finding Tools
2.1. Loading Data
2.2. Available Functions
2.2.1. Mathematical Functions and Dynamic UDFs
2.2.2. Static UDFs
2.3. Available Chromosomal Architectures
2.4. Fitness Functions
2.5. Available Algorithms
2.6. Evolutionary Strategies

3. Working with Classification Tools
3.1. Loading Data
3.2. Available Functions
3.2.1. Mathematical Functions and Dynamic UDFs
3.2.2. Static UDFs
3.3. Available Chromosomal Architectures
3.4. Fitness Functions and the Rounding Threshold
3.5. Available Algorithms
3.6. Evolutionary Strategies

4. Working with Time Series Prediction Tools
4.1. Loading Data
4.2. Available Functions
4.2.1. Mathematical Functions and Dynamic UDFs
4.2.2. Static UDFs
4.3. Available Chromosomal Architectures
4.4. Fitness Functions
4.5. Available Algorithms
4.6. Evolutionary Strategies

5. The Architectures of APS Learning Algorithms
5.1. Gene Expression Programming
5.1.1. An Overview of Gene Expression Programming
5.1.2. The Architecture of GEP Programs
5.1.3. Multigenic Chromosomes and Linking Functions
5.1.4. User Defined Functions
5.2. Gene Expression Programming with Random Numerical Constants
5.2.1. Function Finding and the Creation of Numerical Constants
5.2.2. The Architecture of GEP-RNC Programs
5.3. Karva Notation: The Language of GEP
5.3.1. Karva Notation and the Manipulation of K-expressions
5.3.2. The Symbols of Karva Notation

6. Genetic Operators
6.1. Mutation
6.2. Inversion
6.3. Transposition
6.3.1. IS Transposition
6.3.2. RIS Transposition
6.3.3. Gene Transposition
6.4. Recombination
6.4.1. One-point Recombination
6.4.2. Two-point Recombination
6.4.3. Gene Recombination
6.5. Genetic Operators for Random Numerical Constants
6.5.1. Dc Mutation
6.5.2. Dc Inversion
6.5.3. Dc IS Transposition
6.5.4. Direct Mutation of Random Numerical Constants

7. Evolution from Existing Models
7.1. Optimizing Models Evolved by APS
7.2. Modifying Models Evolved by APS
7.3. Optimizing an External Model
7.4. Adding a Neutral Gene

8. Choosing the Fitness Function
8.1. Fitness Functions for Function Finding
8.1.1. Relative Error Based Fitness Functions
8.1.1.1. Relative Error with Selection Range
8.1.1.2. Relative/Hits
8.1.2. Absolute Error Based Fitness Functions
8.1.2.1. Absolute Error with Selection Range
8.1.2.2. Absolute/Hits
8.1.3. R-square Based Fitness Function
8.1.4. Mean Squared Error Based Fitness Function
8.1.5. Root Mean Squared Error Based Fitness Function
8.1.6. Mean Absolute Error Based Fitness Function
8.1.7. Relative Squared Error Based Fitness Function
8.1.8. Root Relative Squared Error Based Fitness Function
8.1.9. Relative Absolute Error Based Fitness Function
8.1.10. User Defined Fitness Functions
8.2. Fitness Functions for Classification
8.2.1. Number of Hits
8.2.2. Sensitivity/Specificity Based Fitness Function
8.2.3. PPV/NPV Based Fitness Function
8.2.4. R-square Based Fitness Function
8.2.5. Mean Squared Error Based Fitness Function
8.2.6. Root Mean Squared Error Based Fitness Function
8.2.7. Mean Absolute Error Based Fitness Function
8.2.8. Relative Squared Error Based Fitness Function
8.2.9. Root Relative Squared Error Based Fitness Function
8.2.10. Relative Absolute Error Based Fitness Function
8.2.11. User Defined Fitness Functions
8.3. Fitness Functions for Time Series Prediction
8.3.1. Relative Error Based Fitness Functions
8.3.1.1. Relative Error with Selection Range
8.3.1.2. Relative/Hits
8.3.2. Absolute Error Based Fitness Functions
8.3.2.1. Absolute Error with Selection Range
8.3.2.2. Absolute/Hits
8.3.3. R-square Based Fitness Function
8.3.4. Mean Squared Error Based Fitness Function
8.3.5. Root Mean Squared Error Based Fitness Function
8.3.6. Mean Absolute Error Based Fitness Function
8.3.7. Relative Squared Error Based Fitness Function
8.3.8. Root Relative Squared Error Based Fitness Function
8.3.9. Relative Absolute Error Based Fitness Function
8.3.10. User Defined Fitness Functions

9. Analyzing APS Models Statistically
9.1. Statistical Analysis of Function Finding Models
9.1.1. R-square
9.1.2. Correlation Coefficient
9.1.3. Mean Squared Error
9.1.4. Root Mean Squared Error
9.1.5. Mean Absolute Error
9.1.6. Relative Squared Error
9.1.7. Root Relative Squared Error
9.1.8. Relative Absolute Error
9.2. Statistical Analysis of Classification Models
9.2.1. Classification Error
9.2.2. Classification Accuracy
9.2.3. Confusion Matrix
9.2.4. Sensitivity
9.2.5. Specificity
9.2.6. Positive Predictive Value
9.2.7. Negative Predictive Value
9.2.8. R-square
9.2.9. Correlation Coefficient
9.2.10. Mean Squared Error
9.2.11. Root Mean Squared Error
9.2.12. Mean Absolute Error
9.2.13. Relative Squared Error
9.2.14. Root Relative Squared Error
9.2.15. Relative Absolute Error
9.3. Statistical Analysis of Time Series Prediction Models
9.3.1. R-square
9.3.2. Correlation Coefficient
9.3.3. Mean Squared Error
9.3.4. Root Mean Squared Error
9.3.5. Mean Absolute Error
9.3.6. Relative Squared Error
9.3.7. Root Relative Squared Error
9.3.8. Relative Absolute Error

10. Generating Code Automatically
10.1. Endogenous Code
10.1.1. Karva Code
10.1.2. Expression Trees
10.2. Built-in Grammars
10.2.1. C
10.2.2. C++
10.2.3. C#
10.2.4. Visual Basic
10.2.5. VB.Net
10.2.6. Java
10.2.7. Java Script
10.2.8. Fortran
10.3. User Defined Grammars

11. Making Predictions with a Model
11.1. Scoring from Databases
11.2. Scoring from Text Files
11.3. Making Predictions with Time Series Models

12. Settings and Features
12.1. Settings and Features for Function Finding Problems
12.1.1. New Run Wizard
12.1.1.1. Run Category Window
12.1.1.2. Data Source Window
12.1.1.3. Text File Window
12.1.1.4. Database Window
12.1.1.5. Training Query Window
12.1.1.6. Testing Query Window
12.1.1.7. Testing Datasets Window
12.1.2. Data Panel
12.1.2.1. Training Set Tab
12.1.2.2. Testing Set Tab
12.1.3. Settings Panel
12.1.3.1. General Settings Tab
12.1.3.2. Fitness Function Tab
12.1.3.3. Genetic Operators Tab
12.1.3.4. Numerical Constants Tab
12.1.4. Functions Panel
12.1.4.1. Functions (Math) Tab
12.1.4.2. Functions (Math) Tab – Edit DDF Window
12.1.4.3. Static UDFs Tab
12.1.4.4. Static UDFs Tab – Edit UDF Window
12.1.5. Run Panel
12.1.6. History Panel
12.1.7. Results Panel
12.1.7.1. Results Panel – General
12.1.7.2. Grid Option
12.1.7.3. Chart Option
12.1.8. Model Panel
12.1.9. Change Seed Window
12.1.10. Scoring
12.1.10.1. Text File Window
12.1.10.2. Database Window
12.2. Settings and Features for Classification Problems
12.2.1. New Run Wizard
12.2.1.1. Run Category Window
12.2.1.2. Data Source Window
12.2.1.3. Text File Window
12.2.1.4. Database Window
12.1.1.5. Training Query Window
12.1.1.6. Testing Query Window
12.1.1.7. Testing Datasets Window
12.2.2. Data Panel
12.2.2.1. Training Set Tab
12.2.2.2. Testing Set Tab
12.2.3. Settings Panel
12.2.3.1. General Settings Tab
12.2.3.2. Fitness Function Tab
12.2.3.3. Genetic Operators Tab
12.2.3.4. Numerical Constants Tab
12.2.4. Functions Panel
12.2.4.1. Functions (Math) Tab
12.2.4.2. Functions (Math) Tab – Edit DDF Window
12.2.4.3. Static UDFs Tab
12.2.4.4. Static UDFs Tab – Edit UDF Window
12.2.5. Run Panel
12.2.6. History Panel
12.2.7. Results Panel
12.2.7.1. Results Panel – General
12.2.7.2. Grid Option
12.2.7.3. Chart Option
12.2.8. Model Panel
12.1.9. Change Seed Window
12.1.10. Scoring
12.1.10.1. Text File Window
12.1.10.2. Database Window
12.3. Settings and Features for Time Series Prediction Problems
12.3.1. New Run Wizard
12.3.1.1. Run Category Window
12.3.1.2. Data Source Window
12.3.1.3. Text File Window
12.3.1.4. Time Series Transformation Window
12.3.1.5. Testing Datasets Window
12.3.2. Data Panel
12.3.2.1. Transformed Time Series Tab
12.3.2.2. Original Time Series Tab
12.3.3. Settings Panel
12.3.3.1. General Settings Tab
12.3.3.2. Fitness Function Tab
12.3.3.3. Genetic Operators Tab
12.3.3.4. Numerical Constants Tab
12.3.4. Functions Panel
12.3.4.1. Functions (Math) Tab
12.3.4.2. Functions (Math) Tab – Edit DDF Window
12.3.4.3. Static UDFs Tab
12.3.4.4. Static UDFs Tab – Edit UDF Window
12.3.5. Run Panel
12.3.6. History Panel
12.3.7. Predictions Panel
12.3.8. Model Panel
12.1.9. Change Seed Window

13. APS Editions
13.1. Editions
13.2. Requirements
13.3. Installation
13.4. Menus
13.4.1. Run
13.4.2. Edit
13.4.3. View
13.4.4. Panels
13.4.5. Scoring
13.4.6. Help
13.5. License
13.6. Demo

Learning Resources

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