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The Design of Innovation Lessons from and for Competent Genetic Algorithms at Meripustak

The Design of Innovation Lessons from and for Competent Genetic Algorithms by David E. Goldberg , Springer

Books from same Author: David E. Goldberg

Books from same Publisher: Springer

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  • General Information  
    Author(s)David E. Goldberg
    PublisherSpringer
    ISBN9781475736458
    Pages248
    BindingPaperback
    LanguageEnglish
    Publish YearJanuary 2013

    Description

    Springer The Design of Innovation Lessons from and for Competent Genetic Algorithms by David E. Goldberg

    7 69 6 A DESIGN APPROACH TO PROBLEM DIFFICULTY 71 1 Design and Problem Difficulty 71 2 Three Misconceptions 72 3 Hard Problems Exist 76 4 The 3-Way Decomposition and Its Core 77 The Core of Intra-BB Difficulty: Deception 5 77 6 The Core of Inter-BB Difficulty: Scaling 83 7 The Core of Extra-BB Difficulty: Noise 88 Crosstalk: All Roads Lead to the Core 8 89 9 From Multimodality to Hierarchy 93 10 Summary 100 7 ENSURING BUILDING BLOCK SUPPLY 101 1 Past Work 101 2 Facetwise Supply Model I: One BB 102 Facetwise Supply Model II: Partition Success 103 3 4 Population Size for BB Supply 104 Summary 5 106 8 ENSURING BUILDING BLOCK GROWTH 109 1 The Schema Theorem: BB Growth Bound 109 2 Schema Growth Somewhat More Generally 111 3 Designing for BB Market Share Growth 112 4 Selection Press ure for Early Success 114 5 Designing for Late in the Day 116 The Schema Theorem Works 6 118 A Demonstration of Selection Stall 7 119 Summary 122 8 9 MAKING TIME FOR BUILDING BLOCKS 125 1 Analysis of Selection Alone: Takeover Time 126 2 Drift: When Selection Chooses for No Reason 129 3 Convergence Times with Multiple BBs 132 4 A Time-Scales Derivation of Critical Locus 142 5 A Little Model of Noise-Induced Run Elongation 143 6 From Alleles to Building Blocks 147 7 Summary 148 10 DECIDING WELL 151 1 Why is Decision Making a Problem? 151_x000D_ Table of contents :- _x000D_ List of Figures. List of Tables. Preface. Acknowledgments. _x000D_ 1. Genetic Algorithms and Innovation. _x000D_ 2. Making Genetic Algorithms Fly. _x000D_ 3. Three Tools of Conceptual Engineering. _x000D_ 4. Goals and Elements of GA Design. _x000D_ 5. Building Blocks. _x000D_ 6. A Design Approach to Problem Difficulty. _x000D_ 7. Ensuring Building Block Supply. _x000D_ 8. Ensuring Building Block Growth. _x000D_ 9. Making Time for Building Blocks. _x000D_ 10. Deciding Well. _x000D_ 11. Mixing, Control Maps, and GA Success. _x000D_ 12. Design of Competent Genetic Algorithms. _x000D_ Epilogue: from Competence to Efficiency and Beyond. _x000D_ References. Index._x000D_



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