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Advances in Genetic Programming

Cover Image Copyright Year: 1996
Author(s): Angeline, P.; Kinnear, K.
Publisher: MIT Press
Content Type : Books & eBooks
Topics: Computing & Processing (Hardware/Software)
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Abstract

Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers.

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      Front Matter

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): i - xv
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Half Title, Title, Copyright, Contents, Contributors, Preface, Acknowledgments View full abstract»

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      Genetic Programming's Continued Evolution

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 1 - 20
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Evolutionary Computations: The Big Picture, Genetic Programming's Niche, A Proper Introduction to This Volume, Conclusions View full abstract»

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      Variations on the Genetic Programming Theme

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 21
      Copyright Year: 1996

      MIT Press eBook Chapters

      Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. View full abstract»

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      A Comparative Analysis of Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 23 - 44
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Problem Suite and Experimental Methodology, Combining GP crossover with SA or SIHC, Hybridized Algorithms, Reviewing Our Goals View full abstract»

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      Evolving Programmers: The Co-evolution of Intelligent Recombination Operators

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 45 - 68
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Background, The Language, Intelligent Recombination, Experiments, Discussion, Conclusions View full abstract»

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      Extending Genetic Programming with Recombinative Guidance

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 69 - 88
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Recombinative Guidance for GP, Experimental Results, Discussion, Conclusion View full abstract»

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      Two Self-Adaptive Crossover Operators for Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 89 - 109
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction: Adaptive and Self-Adaptive Evolutionary Computations, A Nonstandard Genetic Program, Two Self-Adaptive Crossover Operators, Experiments, Results, Postmortem Parameter Analysis, Discussion, Conclusion View full abstract»

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      Explicitly Defined Introns and Destructive Crossover in Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 111 - 134
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Definitions, The Experimental Setup, Protection Against Destructive Crossover, Experimental Results, Future Work View full abstract»

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      Modular, Recursive and Pruning Genetic Programs

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 135
      Copyright Year: 1996

      MIT Press eBook Chapters

      Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. View full abstract»

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      Simultaneous Evolution of Programs and Their Control Structures

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 137 - 154
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Genetic Programming, Automatically Defined Functions, Macros, Automatically Defined Macros, The Dirt-Sensing, Obstacle-Avoiding Robot, The Lawnmower Problem, When are ADMs Useful?, Wumpus World, Future Work, Conclusions View full abstract»

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      Classifying Protein Segments as Transmembrane Domains Using Architecture-Altering Operations in Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 155 - 176
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction and Overview, Architecture-Altering Operations, Transmembrane Domains in Proteins, Classifying Protein Segments as Transmembrane Domains, Conclusion View full abstract»

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      Discovery of Subroutines in Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 177 - 201
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Background, Adaptive Representation, Learning Good Subroutines: ARL, When to Create Subroutines: Using Entropy, Test Case: Controlling an Agent in a Dynamic Environment, Results, Related Work, Conclusions View full abstract»

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      Evolving Recursive Programs for Tree Search

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 203 - 219
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Background, Adaptive Representation, Learning Good Subroutines: ARL, When to Create Subroutines: Using Entropy, Test Case: Controlling an Agent in a Dynamic Environment, Results, Related Work, Conclusions View full abstract»

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      Evolving Recursive Functions for the Even-Parity Problem Using Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 221 - 240
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Generic Genetic Programming (GGP), Representation of Knowledge for The Even-n-parity Problem, Experiments, Conclusion, Appendix View full abstract»

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      Adaptive Fitness Functions for Dynamic Growing/Pruning of Program Trees

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 241 - 256
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Theoretical Foundation, The Adaptive Fitness Function, Evolutionary Modeling with Neural Trees, Application Results, Discussion View full abstract»

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      Analysis and Implementation Issues in Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 257
      Copyright Year: 1996

      MIT Press eBook Chapters

      Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Efficiently Representing Populations in Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 259 - 278
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Two representations, Experimental Method, Program Size and Variety, A Measure for Subtree Distance, Memory, Representing ADFs in DAG, Conclusion View full abstract»

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      Genetically Optimizing The Speed of Programs Evolved to Play Tetris

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 279 - 298
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Background, Test Problem: Tetris, Strategies that Result with No Time Pressure, Aggregate Computation Time Ceiling, Applying the Aggregate Computation Time Ceiling to Tetris, Method and Results, Variations on the Test Problem, Conclusions and Future Work View full abstract»

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      The Royal Tree Problem, a Benchmark for Single and Multiple Population Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 299 - 316
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Benchmarks, Single Population Results, Coarse-Grain Parallel GPs, Parallel GPs, Overall Discussion View full abstract»

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      Parallel Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 317 - 337
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Selection of Machinery to Implement Parallel Genetic Programming, Implementation of Parallel Genetic Programming Using A Network of Transputers, Comparison of Computational Effort for Different Migration Rates, Increasing Performance: The PowerPC in the Transputer Architecture, Conclusions View full abstract»

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      Massively Parallel Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 339 - 357
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Description of the Implementation, Models for Fitness Evaluation, Selection and Recombination, Results and Performance, Conclusion View full abstract»

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      Type Inheritance in Strongly Typed Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 359 - 375
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, Strongly Typed Genetic Programming, Modifying STGP, Clique Detection, Implementation, Experimental Results, Conclusion, Future Work, Notational Conventions View full abstract»

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      On Using Syntactic Constraints with Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 377 - 394
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, A format to represent syntactic constraints, An illustrative example of syntactic constraints, A practical application of syntactic constraints, Conclusion View full abstract»

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      Data Structures and Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 395 - 414
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, The List Problem, Using GP to Solve the List Problem, Results, Software Maintenance, Discussion, Conclusions, Implementation Issues View full abstract»

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      New Environments for Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 415
      Copyright Year: 1996

      MIT Press eBook Chapters

      Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. View full abstract»

    • Full text access may be available. Click article title to sign in or learn about subscription options.

      Algorithm Discovery using the Genetic Programming Paradigm: Extracting Low-Contrast Curvilinear Features from SAR Images of Arctic Ice

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 417 - 442
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, GP in Context of Scaffolding Algorithm Development, Implementation Overview, GP-Assisted Discovery Using A Dynamic Training Set, Discussion: GP & Scaffolding, Discussion: Domain, Conclusions View full abstract»

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      Genetic Programming Learning and the Cobweb Model

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 443 - 466
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, The Cobweb Model, Population Learning via Genetic Programming, Results of Simulations, The Dynamics of Beliefs: From the Sophisticated to the Simple, Concluding Remarks View full abstract»

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      Evolutionary Identification of Macro-Mechanical Models

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 467 - 488
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, One-dimensional rheological models, Three-dimensional hyperelastic materials, Summary View full abstract»

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      Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 489 - 498
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introduction, The need for automatic derivation of new data fields from raw data, Description of the problem and previous approaches, Methodology of testing, Analysis, Future work View full abstract»

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      Genetic Programming Resources on the World-Wide Web

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 499 - 505
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Web conventions, Anatomy of a URL, Community, Information, Software, Major Archives View full abstract»

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      A Bibliography for Genetic Programming

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 507 - 531
      Copyright Year: 1996

      MIT Press eBook Chapters

      This chapter contains sections titled: Introductions to Genetic Programming, Surveys of Genetic Programming, Early Work on Genetic Algorithms that Evolve Programs, Some Early Genetic Programming References, GP Techniques and Theory, GP Development Systems, GP Applications, Collected Works and Bibliographies, Book Reviews, Comparison With Other Techniques, Patents, Other uses of the term "Genetic Programming", Some Other Genetic Algorithms Approaches to Program Evolution View full abstract»

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      Index

      Angeline, P. ; Kinnear, K.
      Advances in Genetic Programming

      Page(s): 533 - 538
      Copyright Year: 1996

      MIT Press eBook Chapters

      Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming.The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques -- adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation.Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures.The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers. View full abstract»