Advances in genetic programming

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Advances in genetic programming / edited by Kenneth E. Kinnear, Jr.

資料種別:

図書

出版情報:

Cambridge, Mass. : MIT Press, c1994-

形態:

v. ; 24 cm

シリーズ名:

Complex adaptive systems <BA14399342>
Bradford book <BA00245453>

著者名:

ISBN:

9780262111881 [0262111888] ([v. 1])
9780262011587 [0262011581] (v. 2)
9780262194235 [0262194236] (v. 3)

書誌ID:

BA22496297

子書誌情報

フルテキスト

所蔵情報

他の版・巻

書誌詳細

ISSN:

15248828

注記:

Vol. 2: edited by Peter J. Angeline and Kenneth E. Kinnear, Jr.
Vol. 3: edited by Lee Spector ... [et al.]
Includes bibliographical references and indexes
[Vol. 1]: x, 518 p. : ill., c1994. v. 2: xv, 538 p, c1996. v. 3: ix, 476 p. : ill., c1999

主題:

Electronic digital computers--Programming

言語:

英語

目次情報:

Contributors

Acknowledgments

An Introduction to the Third Volume / 1：

A brief overview of genetic programming / 1.1：

Other GP Resources / 1.2：

Public Domain GP Implementations (unsupported) / 1.3：

The work in this volume / 1.4：

Part I: Applications / 1.4.1：

Part II: Theory / 1.4.2：

Part III: Extensions / 1.4.3：

Bibliography

Applications / I：

An Automatic Software Re-Engineering Tool Based on Genetic Programming / 2：

Introduction / 2.1：

Software Re-engineering / 2.1.1：

Parallel Problems / 2.2：

Problems with Data Dependency Analysis / 2.2.1：

Genetic Structure / 2.3：

Atom Mode / 2.3.1：

Atom Mode Transformations. / 2.3.2：

P and S / 2.3.2.1：

F and L / 2.3.2.2：

Shift / 2.3.2.3：

Null/Parnull / 2.3.2.4：

Atom Mode Fitness / 2.3.3：

Directed Analysis for Fpar / 2.3.3.1：

Directed Analysis for Lpar / 2.3.3.2：

Directed Analysis for Pxx / 2.3.3.3：

Directed Analysis / 2.3.3.4：

Loop Mode / 2.3.4：

Loop Fusion / 2.3.4.1：

Loop Shrinking / 2.3.4.2：

Example Individual / 2.4：

Resumption of Atom Mode / 2.4.1：

Directed Data Dependency Analysis / 2.4.3：

Experimental Results / 2.4.4：

Scheduling / 2.4.5：

Conclusion / 2.5：

CAD Surface Reconstruction from Digitized 3D Point Data with a Genetic Programming/Evolution Strategy Hybrid / 3：

Classic Context / 3.1：

Digitizing and Preprocessing / 3.2.1：

Gridded Representation and Topological Information / 3.2.2：

Surreal- a Genetic Programming/Evolution Strategy Hybrid / 3.3：

The Approach / 3.3.1：

Overview / 3.3.2：

Algorithmic Structure / 3.3.3：

Genetic Representation / 3.3.4：

Constructive Solid Geometry / 3.3.4.1：

Terminal and Function Set / 3.3.4.2：

Search Space / 3.3.4.3：

Quality Measure / 3.3.5：

Distance Criterion Delta / 3.3.5.1：

Angle Criterion Abn / 3.3.5.2：

Curvature-type Criterion Ctype / 3.3.5.3：

Primitive-number Criterion Prim / 3.3.5.4：

Variation / 3.3.6：

Mutation / 3.3.6.1：

Recombination / 3.3.6.2：

Creation / 3.3.7：

Selection for variation / 3.3.8：

Selection for the next generation / 3.3.9：

Results / 3.4：

Problem: dowel reconstruction / 3.4.1：

Parameters / 3.4.2：

Discussion / 3.4.3：

Incremental optimization / 3.4.3.1：

Fitness progression / 3.4.3.2：

Population size and convergence / 3.4.3.3：

Interactive evolution / 3.4.3.4：

Problem: cross-structure reconstruction / 3.4.4：

Conclusion and future work / 3.5：

A Genetic Programming Approach for Robust Language Interpretation / 4：

Abstraction on the Problem of Parse Repair / 4.1：

The Basic Problem / 4.2.1：

Program Induction as a Solution / 4.2.2：

ROSE's Application of GP / 4.3：

The Partial Parsing Stage / 4.3.1：

The Combination Stage / 4.3.2：

Applying Genetic Programming / 4.3.2.1：

Fitness Evaluation for the Combination Problem / 4.3.2.2：

Why GP? / 4.4：

Evaluation / 4.5：

Challenges / 4.6：

Acknowledgements

Time Series Modeling Using Genetic Programming: An Application to Rainfall-Runoff Models / 5：

The Genetic Programming System CFG-GP / 5.1：

Rainfall Runoff Modelling / 5.3：

CFG-GP Setup / 5.4：

Catchment Descriptions and Results / 5.5：

The Glan Teifi Catchment / 5.5.1：

The Namoi River Catchment / 5.5.2：

References / 5.5.4：

Automatic Synthesis, Placement, and Routing of Electrical Circuits by Means of Genetic Programming / 6：

Automatic Creation of Circuit Topology and Sizing / 6.1：

Method for Automatic Creation of Circuit Topology, Sizing, Placement, and Routing / 6.3：

The Initial Circuit / 6.3.1：

Circuit-Constructing Functions / 6.3.2：

Component-Creating Functions / 6.3.2.1：

Topology-Modifying Functions / 6.3.2.2：

Development-Controlling Functions / 6.3.2.3：

The Developmental Process / 6.3.3：

Statement of the Illustrative Problem / 6.4：

Preparatory Steps / 6.5：

Initial Circuit / 6.5.1：

Program Architecture / 6.5.2：

Function and Terminal Sets / 6.5.3：

Fitness Measure / 6.5.4：

Control Parameters / 6.5.5：

Termination Criterion and Results Designation / 6.5.6：

Implementation on Parallel Computer / 6.5.7：

Computer Time / 6.6：

Genetic Programming as an Invention Machine / 6.8：

Quantum Computing Applications of Genetic Programming / 6.9：

Quantum Computation / 7.1：

A Virtual Quantum Computer / 7.2：

State Representation and Notation / 7.2.1：

Quantum Gates / 7.2.2：

Quantum NOT and SQUARE ROOT OF NOT / 7.2.2.1：

Applying quantum gates to multi-qubit systems / 7.2.2.2：

Other Quantum Gates / 7.2.2.3：

Running a Quantum Algorithm / 7.2.3：

Example Execution Trace / 7.2.4：

The Power of Quantum Computation / 7.2.5：

Evolving Quantum Algorithms / 7.3：

Standard Tree-based Genetic Programming / 7.3.1：

Stack-Based, Linear Genome Genetic Programming / 7.3.2：

Stackless Linear Genome Genetic Programming / 7.3.3：

Fitness Function / 7.3.4：

Deutsch's Early Promise Problem / 7.4：

The Scaling Majority-On Problem / 7.4.2：

The Database Search Problem / 7.4.3：

The And-Or Query Problem / 7.4.4：

Conclusions / 7.5：

Theory / II：

The Evolution of Size and Shape / 8：

Background / 8.1：

Program Search Spaces / 8.3：

Bloat Inherent in Variable Length Representations / 8.4：

Sextic Polynomial / 8.5：

GP Runs / 8.5.1：

Non GP Search Strategies / 8.5.2：

New Tree Mutation Operators / 8.5.3：

50-150% Fair Mutation Runs / 8.5.3.1：

Subtree Fair Mutation Runs / 8.5.3.2：

Direct Measurement of Genetic Operators Effects on Performance / 8.5.4：

Self Crossover / 8.5.4.1：

Mutation Operators / 8.5.4.2：

Bloat in Discrete Problems / 8.6：

Code Bloat as Protection / 8.6.1：

Code bloat due to "Removal Bias" / 8.6.2：

Evolution of Program Shapes / 8.7：

Future Work / 8.8：

Fitness Distributions: Tools for Designing Efficient Evolutionary Computations / 9：

Fitness distributions / 9.1：

Evolving computer programs using evolutionary programming / 9.4：

Initialization / 9.4.1：

Offspring generation through variation / 9.4.2：

Parent selection / 9.4.3：

Test problems / 9.4.4：

Experiments / 9.4.5：

Analysis of Single-Node (Building) Blocks in Genetic Programming / 9.5：

Current Usages and Definitions / 10.1：

Objectives / 10.1.2：

Case Study Description / 10.2：

Motivations / 10.2.1：

Method / 10.2.2：

Fitness-Centric Experiment / 10.3：

Fitness-Centric Results / 10.3.1：

Fitness-Centric Discussion / 10.3.2：

ERC-Centric Experiment / 10.4：

ERC-Centric Results / 10.4.1：

ERC-Centric Discussion / 10.4.2：

Implications for Building Blocks / 10.5：

Appendix A.10.1 Approaches to Solving f(x) / 10.6：

Appendix A.10.2 Known ERC Strategies

Appendix A.10.3 Alternative Frame for Analyzing GP and ERCs

Rooted-Tree Schemata in Genetic Programming / 11：

Schema Theory / 11.1：

Schemata in genetic algorithms / 11.2.1：

Schemata in genetic programming / 11.2.2：

Portraying Variable Complexity Representations / 11.3：

The rooted-tree schema property / 11.3.1：

Growth of rooted-tree schemata / 11.3.2：

The role of variable size during evolution / 11.3.3：

Adding Parsimony / 11.4：

Selection with a parsimonious fitness function / 11.4.1：

Growth of rooted-tree schemata with parsimony / 11.4.2：

Controlling Schema Growth / 11.5：

Fitness based on pure performance / 11.6：

Parsimonious fitness / 11.6.2：

Adaptive probability of destruction / 11.6.3：

Summary of experiments / 11.6.4：

Solution Acquisition in GP / 11.7：

Related Work / 11.8：

Extensions / 11.9：

Efficient Evolution of Machine Code for CISC Architectures Using Instruction Blocks and Homologous Crossover / 12：

Why Evolve Machine Code? / 12.1：

Advantages of Evolving Machine Code / 12.2.1：

Why is Binary Manipulation so Fast? / 12.3：

Overview of AIM-GP / 12.4：

Making Machine Code Genetic Programming Work on CISC Processors / 12.5：

The Importance of Extending AIM-GP to CISC Processors / 12.5.1：

Challenges in Moving AIM-GP to CISC Processors / 12.5.2：

Instruction Blocks / 12.5.3：

Instruction Annotations / 12.5.4：

The Benefits of ''Glue'' / 12.5.5：

Other AIM-GP Innovations / 12.6：

Memory Access and Large Input Sets / 12.6.1：

Decompilation / 12.6.2：

Homologous Crossover / 12.6.3：

Floating Point Arithmetic / 12.6.4：

Automatically Defined Functions / 12.6.5：

AIM-GP and Tree-Based GP / 12.7：

Summary and Conclusion / 12.8：

Sub-machine-code Genetic Programming / 13：

Sub-machine-code GP / 13.1：

Examples / 13.4：

1-bit and 2-bit Adder Problems / 13.4.1：

Character Recognition Problem / 13.4.2：

Fast Parallel Evaluation of Fitness Cases / 13.4.3：

Appendix: Implementation / 13.6：

Description / 13.A.1：

Code / 13.A.2：

The Internal Reinforcement of Evolving Algorithms / 14：

Neural Programming / 14.1：

The Neural Programming Representation / 14.2.1：

Illustrative Examples / 14.2.2：

Example 1: The Fibonacci Series / 14.2.2.1：

Example 2: The Golden Mean / 14.2.2.2：

Example 3: Foveation / 14.2.2.3：

Internal Reinforcement in NP / 14.3：

Creating a Credit-Blame Map / 14.3.1：

Accumulation of Explicit Credit Scores / 14.3.1.1：

Function Sensitivity Approximation / 14.3.1.2：

Refining the Credit-Blame Map / 14.3.1.3：

Credit Scoring the NP arcs / 14.3.1.4：

Exploration vs. Exploitation Within a Program / 14.3.2：

Using a Credit-Blame Map / 14.3.3：

Mutation: Applying a Credit-Blame Map / 14.3.3.1：

Crossover: Applying a Credit-Blame Map / 14.3.3.2：

The Credit-Blame Map Before/After Refinement / 14.3.4：

IRNP Discussion / 14.3.5：

Experimental Overview / 14.4：

Natural Images / 14.4.2：

Setting PADO up to Solve the Problem / 14.4.2.1：

The Results / 14.4.2.2：

Acoustic Signals / 14.4.3：

Acoustic Signals Revisited / 14.4.3.1：

Inductive Genetic Programming with Immune Network Dynamics / 14.4.4.1：

Immune Version of the GP System / 15.1：

Biological Networks / 15.2.1：

Computational CounterParts in GP / 15.2.2：

The Dynamic Fitness Function / 15.2.3：

Micromechanisms of the Inductive GP / 15.3：

Inductive Learning and Regression / 15.3.1：

Multivariate Trees / 15.3.2：

Context-Preserving Mutation / 15.3.3：

Crossover by Cut and Splice / 15.3.4：

Practical Induction by Immune Dynamics / 15.4：

Traditional and Immune Versions of iGP / 15.4.1：

Performance Measures / 15.4.2：

Machine Learning / 15.4.3：

Time-Series Prediction / 15.4.4：

Relevance to Other Works / 15.5：

A Self-Tuning Mechanism for Depth-Dependent Crossover / 15.7：

The 11MX problem / 16.1：

The ANT problem / 16.3.2：

The robot problem / 16.3.3：

Genetic Recursive Regression for Modeling and Forecasting Real-World Chaotic Time Series / 16.4：

Problem Definition: Data Driven Model Building / 17.1：

Genetic Symbolic Regression and Data Driven Model Building / 17.2：

A New Algorithm Genetic Recursive Regression (GRR) / 17.3：

Recursive Regression / 17.3.1：

Representation of the Regression Model as a Series Expansion / 17.3.2：

Parallel Computational Architecture / 17.3.3：

Adaptive Update of the Numerical Coefficients. / 17.3.4：

Derived Terminal Set / 17.3.5：

Implementation Issues / 17.4：

Fitness Assignment / 17.4.1：

Super Population and Migration between Multiple Populations / 17.4.2：

Dealing with Absurd Attributes of a Symbolic Form / 17.4.3：

Division of the Data Set: Training, Validation and Prediction Regions / 17.4.4：

Termination Conditions / 17.4.5：

Some Manipulations on the Raw Data / 17.4.6：

Application to Read World Chaotic Time Series / 17.5：

Benchmarking / 17.5.1：

Data and Computational Settings / 17.5.1.1：

Benchmarking Results and Discussion / 17.5.1.2：

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 / 17.5.2：

Results and Discussion / 17.5.2.1：

Effects of the Derived Terminal Set (DTS)

Comparisons with Earlier Works / 17.5.3：

ASHRAE and Santa Fe Competitions / 17.5.3.1：

Mackey-Glass Equation / 17.5.3.2：

Conclusion and Issues Remaining / 17.6：

Co-evolutionary Fitness Switching: Learning Complex Collective Behaviors Using Genetic Programming / 18：

Genetic Programming with Coevolutionary Fitness Switching / 18.1：

Results on Table Transport / 18.3：

Application to Robotic Soccer / 18.4：

Evolving Multiple Agents by Genetic Programming / 18.5：

Example Tasks / 19.1：

Fitness Assignment and Breeding Strategies / 19.3：

Comparison with Reinforcement Learning / 19.4：

Evolving Agents with Communication / 19.5：

Evolving Controlling Agents / 19.5.1：

Evolving Negotiating Agents / 19.5.2：

Evolving Other Types of Communicating Agents / 19.6：

Q-learning and Genetic Programming / 19.6.2：

A Multi-agent reinforcement learning / 19.6.3：

Index

Preface

A Perspective on the Work in this Book

What is Evolutionary Computation?

Why is Evolutionary Computation interesting? / 1.1.1：

Styles of Evolutionary Computation / 1.1.2：

What defines Genetic Programming?

Current activity in Genetic Programming

Part II: Increasing the Power of Genetic Programming / 1.3.1：

Part III: Innovative Applications of Genetic Programming / 1.3.2：

Practical Guidance

It isn't as easy as it looks -- but it does work.

The fitness function is exceptionally important.

Representation is important too.

It all comes together in the transmission function. / 1.4.4：

Population size and diversity are also important. / 1.4.5：

Don't generalize from one run. / 1.4.6：

Genetic programming is robust. / 1.4.7：

Know your problem, know your data. / 1.4.8：

Where to go for more information and inspiration. / 1.5：

Biology / 1.5.1：

Complex Adaptive Systems / 1.5.2：

Genetic Algorithms and other Evolutionary Computation paradigms / 1.5.3：

Introduction to Genetic Programming / 1.6：

Introduction to Genetic Algorithms

Program Trees and the LISP Programming Language

Genetic Programming

Automatic Function Definition in Genetic Programming

Sources of Additional Information about Genetic Programming

Sources of Additional Information about Genetic Algorithms / 2.6：

Increasing the Power of Genetic Programming

The Evolution of Evolvability in Genetic Programming

Evolvability / 3.1.1：

Representations / 3.1.2：

Evolving evolvability / 3.1.3：

Constructional selection / 3.1.4：

Synopsis of the models / 3.1.5：

Selection, Transmission, and Evolvability

A general model of the canonical genetic algorithm

Measurement functions

Price's theorem applied to evolvability / 3.2.3：

Price's theorem and the Schema Theorem / 3.2.4：

Dynamics of Genetic Programming

A model of genetic programming dynamics

The "constructional" fitness of a block of code

The constructional fitness is distinct from the marginal fitness

Conservative versus exploratory code

Principle 1 applied to genetic programming

Further work

Notice

Genetic Programming and Emergent Intelligence

Prelude

General Computational Problem Solving

Weak and Strong Methods

Credit Assignment

Evolutionary Weak Methods and Empirical Credit Assignment / 4.2.3：

Emergent Intelligence / 4.2.4：

Genetic Programming: The Inside Story

Comparing Genetic Algorithms and Genetic Programming

Representation of Genotypes / 4.3.1.1：

Complexity of Interpretation / 4.3.1.2：

Syntax Preserving Crossover / 4.3.1.3：

Innate Emergent Intelligence in GP

Emergence of Introns

Emergent Diploidy and Dominance

Exploiting Emergent Intelligence

Emergent Problem Decomposition in Genetic Programs / 4.4.1：

The Genetic Library Builder / 4.4.1.1：

Emergent Evaluation of Modules / 4.4.1.2：

Comparison to ADFs / 4.4.1.3：

Emergence of High-Level Representations / 4.4.1.4：

Generality of Emergent Modules / 4.4.1.5：

Emergent Goal-Directed Behavior / 4.4.2：

Guidelines for Emergent Intelligence

Coaxing Rather than Coercing / 4.5.1：

Hitchhiking as a Technique / 4.5.2：

Opportunism Over Prespecification / 4.5.3：

Explicit Knowledge As A Last Resort / 4.5.4：

Scalable Learning in Genetic Programming using Automatic Function Definition

The Lawn Mower Problem

Preparatory Steps Without Automatic Function Definition

Lawn Size of 64 Without Automatic Function Definition

Lawn Size of 96 Without Automatic Function Definition / 5.4.1：

Lawn Size of 32 Without Automatic Function Definition / 5.4.2：

Preparatory Steps With Automatic Function Definition

Lawn Size of 64 With Automatic Function Definition / 5.6：

Lawn Size of 96 With Automatic Function Definition / 5.6.1：

Lawn Size of 32 With Automatic Function Definition / 5.6.2：

Relationship of Parsimony to Problem Size

Relationship of Computational Effort to Problem Size

Alternatives in Automatic Function Definition: A Comparison of Performance / 5.9：

The Even-4-Parity Problem

Automatic Function Definition: Two Approaches

Automatically Defined Functions: ADF

Module Acquisition: MA

The Genetic Programming Environment

Steady State GP / 6.4.1：

Selection Procedures / 6.4.2：

Genetic Operators / 6.4.3：

Comparisons of ADF and MA strategies

Basic Performance Comparison

Use of a priori Knowledge

Frequency of Function Calls

Functions Including Global Variables

Multiple Use of Parameters

Local vs. Global Function Definitions

Evolution of Function Definitions

Structural Regularity / 6.5.8：

Self-Crossover / 6.5.8.1：

Modular Crossover / 6.5.8.2：

Discussion of Structural Regularity / 6.5.8.3：

Further Work

The Donut Problem: Scalability, Generalization and Breeding Policies in Genetic Programming

Introduction: Depth vs. Breadth

The Donut Problem

Purposely Introduced Imperfections

There is a Solution (Sort of)

Breeding Policies

"Demes" and Spatially Distributed Evolution

Implementation of Distributed Evolution

Elitism and the Steady State Model

Implementation of Steady-State Elitism

Experimental Method

Performance of GP as Class Overlap Increases

Generalization and Uniform Undersampling of the Training Set

Generalization and Nonuniform Sampling of the Training Set

Assessing the Effects of Demes and Elitism

Summary of Experimental Configurations / 7.4.5：

Scalability With Respect to Class Overlap / 7.5.1：

Generalization With Respect to Class Overlap / 7.5.2：

Generalization and Uniform Undersampling of Training Data / 7.5.3：

Generalization and Nonuniformly Distributed Training Data / 7.5.4：

Comparative Performance and the Optimal Function Set / 7.5.5：

Comparative Performance of Breeding Policies / 7.5.6：

Performance Across All Experiments / 7.5.6.1：

Performance Using Uniformly Sparse Training Data / 7.5.6.2：

Performance Using Nonuniform Training Data / 7.5.6.3：

Performance Using Sparse and Nonuniform Training Data / 7.5.6.4：

Performance Using Sparse Data With High Degree of Class Overlap / 7.5.6.5：

Performance Using Non-Sparse Data Sets / 7.5.6.6：

Conclusions About Distributed Evolution / 7.6：

Conclusions Concerning Elitism / 7.6.2：

Comments on the Procedures Used / 7.6.3：

Need for Benchmark Test Functions / 7.6.4：

Big Deme Grids and Other Parameters / 7.6.5：

Gene Frequencies and Distributed Evolution / 7.6.6：

Effects of Locality in Individual and Population Evolution

Domain

Terminal Set / 8.3.1：

Function Set / 8.3.2：

Fitness Evaluation / 8.3.3：

Discussion of Results

Population Seeding

Statistical Analysis

Emergence of Demes

Structural Analysis of Individuals

Recommendations for Future Work

Appendix A: Seed Tank Code

Appendix B: Original Example Tank Code

Appendix C: Stimulus-Response Maps of Example Tanks

The Evolution of Mental Models

The Method and The Model

The Environment

The Implementation

Discussion of Indexed Memory

Discussion of Mental Models

Evolution of Obstacle Avoidance Behavior: Using Noise to Promote Robust Solutions / 9.8：

Previous Work

Obstacle Avoidance as Genetic Programming

The Vehicle

The Obstacle Course

Sensors

Noise / 10.7：

Measuring Fitness in the Presence of Noise / 10.8：

Pygmies and Civil Servants / 10.9：

The Problem Space / 11.1.1：

Genetic Programming Or String GA? / 11.1.2：

Implementation Notes / 11.1.3：

The Benefits of Elitism / 11.1.4：

Traditional Methods

The Fitness Function - Punish or Reward?

Early Results

Maintaining Diversity In Artificial Evolution

Sharing And Crowding

Isolation by Distance

Steady State Genetic Algorithms

Restricted Mating / 11.3.4：

Breeding For Secondary Features / 11.3.5：

Pygmies And Civil Servants / 11.3.6：

Implementation / 11.3.6.1：

Extending the model / 11.3.6.2：

Generalising the model / 11.3.6.3：

Pygmies and Genetic Programming / 11.4.0：

Appendix: The Pygmy Algorithm / 11.5.0：

Acknowlegements

Genetic Programming Using a Minimum Description Length Principle

GP using an MDL principle

Decision Trees and Genetic Programming

MDL-based fitness functions / 12.2.2：

Evolving decision trees / 12.2.3：

Evolving trees with an MDL-based fitness function

Genetic Programming in C++: Implementation Issues

Pointer Based Implementations

A Postfix, Stack-Based Approach

Memory Efficiency / 13.3.1：

Manipulating Postfix Programs / 13.3.2：

Postfix Initialization / 13.3.2.1：

Postfix Crossover / 13.3.2.2：

Postfix Mutation / 13.3.2.3：

The Flow Control Problem with Postfix / 13.3.3：

Mixfix

Prefix Ordering

Initialization, Crossover and Mutation with Prefix / 13.5.1：

Handling Program Flow with Prefix / 13.5.2：

The Node Representation

General Data Support / 13.6.1：

The Opcode Format / 13.6.2：

The Jump Table Mechanism / 13.6.3：

The Prefix, Jump-Table (PJT) Approach / 13.7：

Advanced Topics (Looking for Roadblocks) / 13.8：

Beyond Closure: Handling Multiple Data Types / 13.9.1：

Module Implementation / 13.9.2：

Encapsulation / 13.9.2.1：

Module Execution / 13.9.2.2：

Handling Recursion / 13.9.3：

Simulated Multi-Tasking / 13.9.4：

Using Tables to Evaluate Diversity / 13.9.5：

Conclusion and Future Directions / 13.10：

A Compiling Genetic Programming System that Directly Manipulates the Machine Code

Prologue / 14.0：

Reading Guidance / 14.0.1：

The Compiling Genetic Programming System (CGPS)

The Hardware Environment

The Language for the Genetic Algorithm Implementation

The Structure of a Machine Code Function Callable by a 'C'-function / 14.2.3：

The SPARC Architecture / 14.2.4：

The Instruction Set / 14.2.5：

The Genetic Algorithm / 14.2.6：

Comparison between CGPS and interpreting GP Systems. / 14.2.7：

A Genetic Programming System for Heuristic Classification

Comparison between the CGPS and a Neural Network

The Sample Problem

The Training Set / 14.4.1.1：

Coding of Words for the Genetic Programming System. / 14.4.1.2：

Coding of words for the Neural Network / 14.4.1.3：

The Neural Network

Training Method

Results of comparison

Population Size and Efficiency / 14.4.5：

Applicability

Concluding Remarks / 14.7：

Biblography

Innovative Applications of Genetic Programming

Automatic Generation of Programs for Crawling and Walking

The problem

The approach

Functions and terminals

Side-effecting functions and simulated memory

Constant perturbation

Fitness evaluation

Program structure / 15.3.5：

Experiment 1 / 15.3.6：

Experiment 2

Experiment 3

Analysis of the results

Analysis of the method

Comparison with random search / 15.6.1：

Comparison with other methods / 15.6.2：

Practical considerations / 15.6.3：

Scalability / 15.6.3.1：

Real-Time control / 15.6.3.2：

Genetic Programming for the Acquisition of Double Auction Market Strategies

Double Auction Markets and the Santa Fe Tournaments

The Double Auction Mechanism / 16.1.1：

Measuring Trading Efficiency / 16.1.2：

The Santa Fe Tournaments / 16.1.3：

Structure of Local Double Auctions / 16.1.4：

The Local Experiments / 16.1.5：

Genetic Programming of Strategies

The GP Environment / 16.2.1：

The Programming Constructs / 16.2.2：

GP Selection Parameters / 16.2.3：

A Comparable Simulated Annealing Environment / 16.2.4：

Is Genetic Search Useful ?

Economising on Fitness Evaluations

Two Scientific Applications of Genetic Programming: Stack Filters and Non-Linear Equation Fitting to Chaotic Data

Development of Stack Filters

Methods / 17.2.1：

Fitting of Non-Linear Equations to Chaotic Data / 17.2.3：

Deception and Fitness / 17.3.4.1：

Fitness Measures / 17.3.4.2：

Effectiveness of Prediction / 17.3.4.3：

Structural Insights / 17.3.4.4：

Conclusions and Prospects

The Automatic Generation of Plans for a Mobile Robot via Genetic Programming with Automatically Defined Functions

The Genetic Planner

An Example World: A Robot on a 2-D Grid.

A set of procedurally-defined operators / 18.3.1：

A set of predicates that describe the world / 18.3.2：

Fitness functions for each of the predicates. / 18.3.3：

A ground goal expression / 18.3.4：

A simulation of the world / 18.3.5：

A Demonstration of The Genetic Planner

Analysis of Some Best-Of-Run ADFs / 18.6：

Competitively Evolving Decision Trees Against Fixed Training Cases for Natural Language Processing / 18.7：

The Domain: Word Sense Disambiguation

The Training Cases

How Decision Trees Work

Crossover Operations on Decision Trees

How Fixed Training Data Participate in Competitive Adaptation

Averting Overlearning with Decision Trees: Fitness Penalty

Non-trivial Learning and Generalization Performance / 19.8：

Competition / 19.8.2：

Fitness Penalty / 19.8.3：

Linguistic Data / 19.8.4：

Cracking and Co-Evolving Randomizers / 19.9：

Motivation / 20.1：

Arguments for Success / 20.3：

Two Player Penny Matching Game / 20.3.1：

Uniform Distribution / 20.3.2：

Models / 20.4：

Two Player Multi-Penny Matching Game / 20.4.1：

Single Generator / 20.4.2：

Separate Generators and Guessers / 20.4.4：

Sexing Populations / 20.4.5：

New Techniques / 20.5：

Dynamic Sampling / 20.5.1：

Fitness Tournament / 20.5.2：

Tested Randomizers / 20.6：

Tableau / 20.6.2：

Functions and Terminals / 20.6.3：

GP Shell Modifications / 20.6.4：

Optimizing Confidence of Text Classification by Evolution of Symbolic Expressions / 20.7：

The News Story Classification Problem / 21.1：

Automated keyword assignment using MBR / 21.3：

The Coding Algorithm / 21.4：

The Referral Problem / 21.5：

Referral with single keyword per document / 21.5.1：

Referral with multiple keywords / 21.5.2：

Brief Overview of Genetic Algorithms / 21.6：

Example formulae / 21.7：

example results / 21.7.0.1：

Representation of evolved formulae / 21.7.1：

The environment for Genetic evolution / 21.8：

Generation of the initial random populations / 21.8.1：

Evaluating fitness / 21.8.2：

Fitness proportionate reproduction / 21.8.3：

Cross-over / 21.8.4：

Population size / 21.8.5：

Number of generations / 21.8.7：

The test environment / 21.9：

Discussion of results / 21.10：

Continuing and Future Work / 21.11：

Evolvable 3D Modeling for Model-Based Object Recognition Systems / 22：

Evolvable 3D Modeling with Multi-level GP/GA / 22.1：

Evolvable modeling of jetliners--implementation / 22.3：

The Data Structure of the Evolvable 3D Jet Models / 22.3.1：

The Parameter Constraints and Parametric Relations / 22.3.2：

The Population Structure and Population Transition / 22.3.3：

Related Works, Extensions, and Applications / 22.4：

Comments and Conclusions / 22.5：

Automatically Defined Features: The Simultaneous Evolution of 2-Dimensional Feature Detectors and an Algorithm for Using Them / 23：

Introduction and Overview / 23.1：

Hit-Miss Matrices and the Two-Dimensional Genetic Algorithm / 23.2：

Hit-miss Matrices in OCR / 23.2.1：

Hit-Miss Matrices in GP / 23.2.2：

Evolutionary Operators for Hit-Miss Matrices / 23.2.3：

Definition of Three Test Problem Sets / 23.3：

Problem Set 1: The {L,T} problem: / 23.3.1：

Problem Set 2: The single example digit discrimination task: / 23.3.2：

Problem Set 3: The multiple example digit discrimination task: / 23.3.3：

Method for applying GP and GA to digit recognition / 23.4：

Functions, Terminals, and Basic Architecture / 23.4.1：

Methods of generation and application of the genetic operators / 23.4.2：

Example of an individual / 23.4.3：

The fitness function / 23.4.4：

Values of run parameters and the success predicate / 23.4.5：

Problem Set #2: Single example digit discrimination problem / 23.5：

Problem Set #3: Multiple example digit discrimination tasks / 23.5.3：

Conclusions and Future work / 23.6：

Genetic Micro Programming of Neural Networks / 24：

Review of Cellular Encoding / 24.1：

JaNNeT: A Neural Compiler of Pascal Program / 24.3：

A Hierarchy of Genetic Languages / 24.5：

Cellular Encoding Versus LISP / 24.6：

Author Index / 24.7：

Contributors

Acknowledgments

An Introduction to the Third Volume / 1：

A brief overview of genetic programming / 1.1：

Other GP Resources / 1.2：

Public Domain GP Implementations (unsupported) / 1.3：

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