brkga - AT&T Labs Research

More documents

Recommendations

Info

Framework for biased random-key genetic algorithmsGenerate P vectorsof random keysDecode each vectorof random keysClassify solutions aselite or non-eliteSort solutions bytheir costsnoStopping rulesatisfied?yesstopCopy elite solutionsto next populationGenerate mutants innext populationCombine elite andnon-elite solutionsand add children tonext populationLION 7 ✤ January 2013BRKGA
Framework for biased random-key genetic algorithmsProblem independentGenerate P vectorsof random keysDecode each vectorof random keysClassify solutions aselite or non-eliteSort solutions bytheir costsnoStopping rulesatisfied?yesstopCopy elite solutionsto next populationGenerate mutants innext populationCombine elite andnon-elite solutionsand add children tonext populationLION 7 ✤ January 2013BRKGA
Page 1 and 2:
Biased random-key geneticalgorithms
Page 3 and 4:
Summary• Metaheuristics and basic
Page 5 and 6:
MetaheuristicsMetaheuristics are hi
Page 7 and 8:
Genetic algorithmsLION 7 ✤ Januar
Page 9 and 10:
Genetic algorithmsIndividual: solut
Page 11 and 12:
Genetic algorithmsGenetic algorithm
Page 13 and 14:
Genetic algorithmsProbability of se
Page 15 and 16:
Crossover and mutationmutationaComb
Page 17 and 18:
Crossover and mutationmutationaComb
Page 19 and 20:
Evolution of solutionsLION 7 ✤ Ja
Page 21 and 22:
Page 23 and 24:
Page 25 and 26:
Page 27 and 28:
Encoding with random keys• A rand
Page 29 and 30:
Encoding with random keys• A rand
Page 31 and 32:
Encoding with random keys: Sequenci
Page 33 and 34:
Encoding with random keys: Sequenci
Page 35 and 36: Encoding with random keys: Subsetse
Page 37 and 38: Encoding with random keys: Assignin
Page 39 and 40: Encoding with random keys: Assignin
Page 41 and 42: GAs and random keys• Introduced b
Page 43 and 44: GAs and random keys• Introduced b
Page 45 and 46: GAs and random keys• Mating is do
Page 53 and 54: GAs and random keysInitial populati
Page 55 and 56: GAs and random keysAt the K-th gene
Page 57 and 58: GAs and random keysAt the K-th gene
Page 59 and 60: GAs and random keysEvolutionary dyn
Page 61 and 62: GAs and random keysEvolutionary dyn
Page 63 and 64: Biased random key genetic algorithm
Page 65 and 66: How RKGA & BRKGA differRKGAboth par
Page 67 and 68: How RKGA & BRKGA differRKGAboth par
Page 69 and 70: Paper comparing BRKGA and Bean'sMet
Page 71 and 72: Pr(t BRKGA≤ t RKGA) = 0.740Probab
Page 73 and 74: Pr(t BRKGA≤ t RKGA) = 0.999set co
Page 75 and 76: Pr(t BRKGA≤ t RKGA) = 0.733set co
Page 77 and 78: set k-coveringproblem: scp41-2Pr(t
Page 79 and 80: Pr(t BRKGA≤ t RKGA) = 0.881set k-
Page 81 and 82: Pr(t BRKGA≤ t RKGA) = 0.847set k-
Page 83 and 84: Observations• Random method: keys
Page 85: Observations• Random method: keys
Page 89 and 90: Decoding of random key vectors can
Page 91 and 92: solutionNetwork monitor location pr
Page 93 and 94: Randomized heuristic iterationcount
Page 95 and 96: In most of the independent runs, th
Page 97 and 98: In most of the independent runs, th
Page 99 and 100: However, some runs take much longer
Page 101 and 102: However, some runs take much longer
Page 103 and 104: Probability that algorithm will tak
Page 105 and 106: Probability that algorithm will sti
Page 107 and 108: Restart strategies• First propose
Page 109 and 110: Restart strategy for BRKGA• Recal
Page 111 and 112: Example of restart strategy for BRK
Page 123 and 124: Specifying a biased random-key GA
Page 125 and 126: Specifying a biased random-key GA
Page 127 and 128: Specifying a biased random-key GAPa
Page 135 and 136: kgaAPI: A C++ API for BRKGA• Effi
Page 137 and 138:
kgaAPI: A C++ API for BRKGA• Effi
Page 139 and 140:
An example BRKGA:Packing weightedre
Page 141 and 142:
Constrained orthogonal packing• G
Page 143 and 144:
Constrained orthogonal packing• G
Page 145 and 146:
Constrained orthogonal packing• r
Page 147 and 148:
Constrained orthogonal packing• r
Page 149 and 150:
ObjectiveAmong the many feasible pa
Page 151 and 152:
ObjectiveAmong the many feasible pa
Page 153 and 154:
ApplicationsProblem arises in sever
Page 155 and 156:
Hopper & Turton, 2001Instance 4-2 6
Page 157 and 158:
Hopper & Turton, 2001Instance 4-2 6
Page 159 and 160:
BRKGA forconstrained 2-dimorthogona
Page 161 and 162:
Encoding• Solutions are encoded a
Page 163 and 164:
Decoding• Simple heuristic to pac
Page 165 and 166:
Decoding• Simple heuristic to pac
Page 167 and 168:
Decoding• A maximal empty rectang
Page 169 and 170:
132 4BL can run into problems eveno
Page 171 and 172:
433412RTPS: 1-2-4-312RTPS: 1-2-3-42
Page 173 and 174:
Decoding• If LB is used, ERSs are
Page 175 and 176:
1BL2BL3LB4BLERS[1]LION 7 ✤ Januar
Page 177 and 178:
2BL3LB4BL1BLERS[1]LION 7 ✤ Januar
Page 179 and 180:
3LB4BL1BL2BLLION 7 ✤ January 2013
Page 181 and 182:
3LB4BLERS[2]1BL2BLLION 7 ✤ Januar
Page 183 and 184:
4BL3LBERS[1]1BL2BLLION 7 ✤ Januar
Page 185 and 186:
4BL4 does fitin ERS[2].3LBERS[2]1BL
Page 187 and 188:
Experimental resultsLION 7 ✤ Janu
Page 189 and 190:
Design• We compare solution value
Page 191 and 192:
Design• We compare solution value
Page 193 and 194:
Number of best solutions / total in
Page 195 and 196:
New BKSfor a 100 x100doublyconstrai
Page 197 and 198:
Some remarksWe have extended this t
Page 199 and 200:
Literature• BRKGAs have been appl
Page 201 and 202:
Telecommunications• Routing: Eric
Page 203 and 204:
Scheduling• Job-shop scheduling:
Page 205 and 206:
Network optimization• Concave min
Page 207 and 208:
Packing• 2D orthogonal packing: G
Page 209 and 210:
Transportation• Tollbooth assignm
Page 211 and 212:
Automatic parameter tuning• GRASP
Page 213 and 214:
Software• C++ API: Toso and R. (2
show all

brkga - AT&T Labs Research

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?