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Computer Science - Theoretical Computer Science | The Design of Innovation - Lessons from and for Competent Genetic Algorithms

The Design of Innovation

Lessons from and for Competent Genetic Algorithms

Goldberg, David E.

Softcover reprint of the original 1st ed. 2002, XXIV, 248 p.

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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

Content Level » Research

Keywords » Building Block Supply - Building Blocks - Competent Genetic Algorithms - Efficient Genetic Algorithms - Evolutionary Computing - Gene Expression - Mathematica - algorithms - behavior - complexity - control - evolution - evolutionary computation - genetic algorithms - s

Related subjects » Artificial Intelligence - Mathematics - Statistics - Theoretical Computer Science

Table of contents 

List of Figures. List of Tables. Preface. Acknowledgments. 1. Genetic Algorithms and Innovation. 2. Making Genetic Algorithms Fly. 3. Three Tools of Conceptual Engineering. 4. Goals and Elements of GA Design. 5. Building Blocks. 6. A Design Approach to Problem Difficulty. 7. Ensuring Building Block Supply. 8. Ensuring Building Block Growth. 9. Making Time for Building Blocks. 10. Deciding Well. 11. Mixing, Control Maps, and GA Success. 12. Design of Competent Genetic Algorithms. Epilogue: from Competence to Efficiency and Beyond. References. Index.

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