The Design of Innovation: Lessons from and for Competent Genetic Algorithmsby David E. Goldberg7 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 |
Contents
V | |
VII | |
VIII | |
IX | 1 |
X | 6 |
XI | 7 |
XII | 8 |
XIII | 14 |
LI | 102 |
LII | 104 |
LIII | 105 |
LIV | 111 |
LVI | 112 |
LVII | 115 |
LVIII | 118 |
LIX | 128 |
XIV | 15 |
XV | 19 |
XVI | 28 |
XVII | 34 |
XVIII | 36 |
XIX | 39 |
XX | 40 |
XXI | 42 |
XXII | 44 |
XXIII | 45 |
XXIV | 46 |
XXV | 47 |
XXVI | 48 |
XXVII | 52 |
XXVIII | 55 |
XXIX | 57 |
XXX | 62 |
XXXI | 63 |
XXXIII | 69 |
XXXIV | 74 |
XXXV | 75 |
XXXVI | 79 |
XXXVII | 86 |
XXXVIII | 87 |
XLI | 88 |
XLII | 89 |
XLIII | 90 |
XLIV | 92 |
XLV | 95 |
XLVIII | 97 |
XLIX | 98 |
L | 100 |
LX | 129 |
LXI | 133 |
LXII | 134 |
LXIII | 137 |
LXV | 139 |
LXVI | 142 |
LXVII | 143 |
LXVIII | 151 |
LXIX | 155 |
LXXI | 156 |
LXXII | 158 |
LXXIII | 164 |
LXXIV | 169 |
LXXV | 170 |
LXXVI | 173 |
LXXVIII | 174 |
LXXIX | 179 |
LXXX | 187 |
LXXXI | 188 |
LXXXII | 191 |
LXXXIII | 195 |
LXXXIV | 201 |
LXXXV | 203 |
LXXXVI | 204 |
LXXXVII | 206 |
LXXXVIII | 208 |
LXXXIX | 211 |
XC | 213 |
XCI | 215 |
XCII | 227 |
Other editions - View all
The Design of Innovation: Lessons from and for Competent Genetic Algorithms David Edward Goldberg No preview available - 2002 |
The Design of Innovation: Lessons from and for Competent Genetic Algorithms David E. Goldberg No preview available - 2013 |
Common terms and phrases
alleles analysis average fitness Bayesian Bayesian networks bits bound building blocks calculate Cantú-Paz chapter competent GAs competent genetic algorithms complex conceptual consider control map convergence crossover crossover operators crossover probability crosstalk decision derived design decomposition Design of Innovation dimensional dimensional analysis discussed drift effective engineering epistasis equation evolution strategies Evolutionary Computation example exogenous noise facetwise models fast messy Figure fmGA gambler's ruin gemGA gene generation-wise genetic algorithms genetic and evolutionary genetic drift Goldberg hard problems Harik I-constant idea IlliGAL Report initial Kargupta linkage learning little models LLGA market share Markov chain mechanisms messy genetic algorithm mixing multimodality OneMax problem optimization parameter partition Pelikan population sizes population-sizing probabilistic problem difficulty proportion proportionate selection recombination salience scaling difficulty schema theorem selection pressure selection schemes simple solution solve strings subfunctions subquadratic takeover Thierens tion tournament selection trap function truncation selection understand values variables variance Wright brothers