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9780262194235

Advances in Genetic Programming - Vol. 3

by
  • ISBN13:

    9780262194235

  • ISBN10:

    0262194236

  • Format: Hardcover
  • Copyright: 1999-07-16
  • Publisher: Bradford Books
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List Price: $80.00

Summary

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time- and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programminghighlights many of the recent technical advances in this increasingly popular field.

Table of Contents

Contributors
Acknowledgments
An Introduction to the Third Volume
A brief overview of genetic programming
Other GP Resources
Public Domain GP Implementations (unsupported)
The work in this volume
Part I: Applications
Part II: Theory
Part III: Extensions
Bibliography
Applications
An Automatic Software Re-Engineering Tool Based on Genetic Programming
Introduction
Software Re-engineering
Parallel Problems
Problems with Data Dependency Analysis
Genetic Structure
Atom Mode
Atom Mode Transformations.
P and S
F and L
Shift
Null/Parnull
Atom Mode Fitness
Directed Analysis for Fpar
Directed Analysis for Lpar
Directed Analysis for Pxx
Directed Analysis
Loop Mode
Loop Fusion
Loop Shrinking
Example Individual
Loop Mode
Resumption of Atom Mode
Directed Data Dependency Analysis
Experimental Results
Scheduling
Conclusion
Bibliography
CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid
Introduction
Classic Context
Digitizing and Preprocessing
Gridded Representation and Topological Information
Surreal- a Genetic Programming/Evolution Strategy Hybrid
The Approach
Overview
Algorithmic Structure
Genetic Representation
Constructive Solid Geometry
Terminal and Function Set
Search Space
Quality Measure
Distance Criterion Delta
Angle Criterion Abn
Curvature-type Criterion Ctype
Primitive-number Criterion Prim
Variation
Mutation
Recombination
Creation
Selection for variation
Selection for the next generation
Results
Problem: dowel reconstruction
Parameters
Discussion
Incremental optimization
Fitness progression
Population size and convergence
Interactive evolution
Problem: cross-structure reconstruction
Conclusion and future work
Acknowledgments
Bibliography
A Genetic Programming Approach for Robust Language Interpretation
Introduction
Abstraction on the Problem of Parse Repair
The Basic Problem
Program Induction as a Solution
ROSE's Application of GP
The Partial Parsing Stage
The Combination Stage
Applying Genetic Programming
Fitness Evaluation for the Combination Problem
Why GP?
Evaluation
Challenges
Acknowledgements
Bibliography
Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models
Introduction
The Genetic Programming System CFG-GP
Rainfall Runoff Modelling
CFG-GP Setup
Catchment Descriptions and Results
The Glan Teifi Catchment
Results
The Namoi River Catchment
Results
Discussion
Conclusion
Acknowledgments
References
Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming
Introduction
Automatic Creation of Circuit Topology and Sizing
Method for Automatic Creation of Circuit Topology, Sizing, Placement, and Routing
The Initial Circuit
Circuit-Constructing Functions
Component-Creating Functions
Topology-Modifying Functions
Development-Controlling Functions
The Developmental Process
Statement of the Illustrative Problem
Preparatory Steps
Initial Circuit
Program Architecture
Function and Terminal Sets
Fitness Measure
Control Parameters
Termination Criterion and Results Designation
Implementation on Parallel Computer
Results
Computer Time
Genetic Programming as an Invention Machine
Conclusion
Bibliography
Quantum Computing Applications of Genetic Programming
Quantum Computation
A Virtual Quantum Computer
State Representation and Notation
Quantum Gates
Quantum NOT and SQUARE ROOT OF NOT
Applying quantum gates to multi-qubit systems
Other Quantum Gates
Running a Quantum Algorithm
Example Execution Trace
The Power of Quantum Computation
Evolving Quantum Algorithms
Standard Tree-based Genetic Programming
Stack-Based, Linear Genome Genetic Programming
Stackless Linear Genome Genetic Programming
Fitness Function
Results
Deutsch's Early Promise Problem
The Scaling Majority-On Problem
The Database Search Problem
The And-Or Query Problem
Conclusions
Acknowledgements
Bibliography
Theory
The Evolution of Size and Shape
Introduction
Background
Program Search Spaces
Bloat Inherent in Variable Length Representations
Sextic Polynomial
GP Runs
Non GP Search Strategies
New Tree Mutation Operators
50-150% Fair Mutation Runs
Subtree Fair Mutation Runs
Direct Measurement of Genetic Operators Effects on Performance
Self Crossover
Mutation Operators
Bloat in Discrete Problems
Code Bloat as Protection
Code bloat due to "Removal Bias"
Evolution of Program Shapes
Discussion
Conclusions
Future Work
Acknowledgments
Bibliography
Fitness Distributions: Tools for Designing Efficient Evolutionary Computations
Introduction
Background
Fitness distributions
Evolving computer programs using evolutionary programming
Initialization
Offspring generation through variation
Parent selection
Test problems
Experiments
Results
Discussion
Conclusion
Acknowledgments
Bibliography
Analysis of Single-Node (Building) Blocks in Genetic Programming
Introduction
Current Usages and Definitions
Objectives
Case Study Description
Motivations
Method
Fitness-Centric Experiment
Fitness-Centric Results
Fitness-Centric Discussion
ERC-Centric Experiment
ERC-Centric Results
ERC-Centric Discussion
Implications for Building Blocks
Conclusions
Acknowledgments
Appendix A.10.1 Approaches to Solving f(x)
Appendix A.10.2 Known ERC Strategies
Appendix A.10.3 Alternative Frame for Analyzing GP and ERCs
Bibliography
Rooted-Tree Schemata in Genetic Programming
Introduction
Schema Theory
Schemata in genetic algorithms
Schemata in genetic programming
Portraying Variable Complexity Representations
The rooted-tree schema property
Growth of rooted-tree schemata
The role of variable size during evolution
Adding Parsimony
Selection with a parsimonious fitness function
Growth of rooted-tree schemata with parsimony
Controlling Schema Growth
Experimental Results
Fitness based on pure performance
Parsimonious fitness
Adaptive probability of destruction
Summary of experiments
Solution Acquisition in GP
Related Work
Conclusion
Acknowledgements
Bibliography
Extensions
Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover
Introduction
Why Evolve Machine Code?
Advantages of Evolving Machine Code
Why is Binary Manipulation so Fast?
Overview of AIM-GP
Making Machine Code Genetic Programming Work on CISC Processors
The Importance of Extending AIM-GP to CISC Processors
Challenges in Moving AIM-GP to CISC Processors
Instruction Blocks
Instruction Annotations
The Benefits of ''Glue''
Other AIM-GP Innovations
Memory Access and Large Input Sets
Decompilation
Homologous Crossover
Floating Point Arithmetic
Automatically Defined Functions
AIM-GP and Tree-Based GP
Future Work
Summary and Conclusion
Acknowledgments
Bibliography
Sub-machine-code Genetic Programming
Introduction
Background
Sub-machine-code GP
Examples
1-bit and 2-bit Adder Problems
Character Recognition Problem
Discussion
Fast Parallel Evaluation of Fitness Cases
Conclusions
Appendix: Implementation
Description
Code
Acknowledgements
Bibliography
The Internal Reinforcement of Evolving Algorithms
Introduction
Neural Programming
The Neural Programming Representation
Illustrative Examples
Example 1: The Fibonacci Series
Example 2: The Golden Mean
Example 3: Foveation
Internal Reinforcement in NP
Creating a Credit-Blame Map
Accumulation of Explicit Credit Scores
Function Sensitivity Approximation
Refining the Credit-Blame Map
Credit Scoring the NP arcs
Exploration vs. Exploitation Within a Program
Using a Credit-Blame Map
Mutation: Applying a Credit-Blame Map
Crossover: Applying a Credit-Blame Map
The Credit-Blame Map Before/After Refinement
IRNP Discussion
Experimental Results
Experimental Overview
Natural Images
Setting PADO up to Solve the Problem
The Results
Acoustic Signals
Setting PADO up to Solve the Problem
The Results
Acoustic Signals Revisited
Setting PADO up to Solve the Problem
The Results
Related Work
Conclusions
Acknowledgements
Bibliography
Inductive Genetic Programming with Immune Network Dynamics
Introduction
Immune Version of the GP System
Biological Networks
Computational CounterParts in GP
The Dynamic Fitness Function
Micromechanisms of the Inductive GP
Inductive Learning and Regression
Multivariate Trees
Context-Preserving Mutation
Crossover by Cut and Splice
Practical Induction by Immune Dynamics
Traditional and Immune Versions of iGP
Performance Measures
Machine Learning
Time-Series Prediction
Discussion
Relevance to Other Works
Conclusion
Bibliography
A Self-Tuning Mechanism for Depth-Dependent Crossover
Introduction
A Self-Tuning Mechanism for Depth-Dependent Crossover
Experimental Results
The 11MX problem
The ANT problem
The robot problem
Discussion
Conclusion
Acknowledgments
Bibliography
Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series
Problem Definition: Data Driven Model Building
Genetic Symbolic Regression and Data Driven Model Building
A New Algorithm Genetic Recursive Regression (GRR)
Recursive Regression
Representation of the Regression Model as a Series Expansion
Parallel Computational Architecture
Adaptive Update of the Numerical Coefficients.
Derived Terminal Set
Implementation Issues
Fitness Assignment
Super Population and Migration between Multiple Populations
Dealing with Absurd Attributes of a Symbolic Form
Division of the Data Set: Training, Validation and Prediction Regions
Termination Conditions
Some Manipulations on the Raw Data
Application to Read World Chaotic Time Series
Benchmarking
Data and Computational Settings
Benchmarking Results and Discussion
Effects of the Adaptive Update of the Numerical Coefficients
Effects of the Parallel Architecture
Effects of the Recursive Model Building
Rend World Chaotic Time Series
Data and Computational Settings
Results and Discussion
Effects of the Derived Terminal Set (DTS)
Comparisons with Earlier Works
ASHRAE and Santa Fe Competitions
Mackey-Glass Equation
Conclusion and Issues Remaining
Acknowledgments
Bibliography
Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming
Introduction
Genetic Programming with Coevolutionary Fitness Switching
Results on Table Transport
Application to Robotic Soccer
Conclusions
Acknowledgements
Bibliography
Evolving Multiple Agents by Genetic Programming
Introduction
Example Tasks
Fitness Assignment and Breeding Strategies
Comparison with Reinforcement Learning
Evolving Agents with Communication
Evolving Controlling Agents
Evolving Negotiating Agents
Discussion
Evolving Other Types of Communicating Agents
Q-learning and Genetic Programming
Related Work
Conclusion
A Multi-agent reinforcement learning
Bibliography
Index
Table of Contents provided by Publisher. All Rights Reserved.

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