brkga - AT&T Labs Research

More documents

Recommendations

Info

GAs and random keys• Mating is done usingparametrized uniformcrossover (Spears & DeJong , 1990)• For each gene, flip a biasedcoin to choose whichparent passes the allele(key, or value of gene) tothe child.a = ( 0.25, 0.19, 0.67, 0.05, 0.89 )b = ( 0.63, 0.90, 0.76, 0.93, 0.08 )c = ( 0.25, 0.90 )LION 7 ✤ January 2013BRKGA
GAs and random keys• Mating is done usingparametrized uniformcrossover (Spears & DeJong , 1990)• For each gene, flip a biasedcoin to choose whichparent passes the allele(key, or value of gene) tothe child.a = ( 0.25, 0.19, 0.67, 0.05, 0.89 )b = ( 0.63, 0.90, 0.76, 0.93, 0.08 )c = ( 0.25, 0.90, 0.76 )LION 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 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 47: GAs and random keys• Mating is do
Page 51 and 52: 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 and 86: Observations• Random method: keys
Page 87 and 88: Framework for biased random-key gen
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 113 and 114:
Page 115 and 116:
Page 117 and 118:
Page 119 and 120:
Page 121 and 122:
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 129 and 130:
Page 131 and 132:
Page 133 and 134:
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

Create successful ePaper yourself

Delete template?

Save as template?