ga example
DESCRIPTION
Genetic algorithms exampleTRANSCRIPT
GENETIC ALGORITHM Population parameters Diversity parameters Population size 30 Number of bits per parameter 30 Maximum number of generations 50 Mutation probability 0.1 Number of parameters 2 Cross over probability 0.3 Possibilities per parameter 32768 Creep mutation probability 0.04 Maximum parameter value 1 Minimum parameter value 0
Selection Parameters Random number generator seed 30 Number of children 2
Stopping criterionMinimum fitness improvement MFI 0.0500%Number of repetitions 50
Modified GA EXPERIMENTATION Minimum fitness improvement MFI 3.000% Parameter to explore Seed Number of Generations control (t) 2 Increment 100 population increase control (times) 2 Experiments 3 Population injections 10
Ploting options
seed ObjectiveFunction
Evaluations
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EXPERIMENT RESULTS
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Uniform crossover
Elitism
Creep mutation
micro-GA
Use niching
Plot the best from each generation
Parameters Required in Injection Option
Parameters Required in Experimentation Option
30 1500 0.9974856130 1500 0.9982135230 1500 0.999074
0 50 100 150 200 2500
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EXPERIMENT RESULTS
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SIMPLE GENETIC ALGORITHM Population parameters Population size 40 Parameters controlling diversity Maximum number of generations 800 Mutation probability 0.07 Number of variables 2 Cross over probability 0.5 Number of binary digits 10 Minimum parameter value 4 Maximum parameter value 6 Initial seed 3000
Stoping criterion EXPERIMENTATION Minimum fitness improvement MF 0.0500% Parameter to explore Seed Number of repetitions 50 Increment 100 Experiments 2
Ploting options
Function evaluations 2400 Maximum 1
seed objective
3000 2760 0.5053483100 2400 1
function evaluations
2980 3000 3020 3040 3060 3080 3100 31202200
2300
2400
2500
2600
2700
2800
EXPERIMENT RESULTS
seed
Fu
nc
tio
n E
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lua
tio
ns
Plot the best from each generation
Parameters Required in Experimentation Option
2980 3000 3020 3040 3060 3080 3100 31202200
2300
2400
2500
2600
2700
2800
EXPERIMENT RESULTS
seed
Fu
nc
tio
n E
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lua
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ns
DIFFERENTIAL EVOLUTION ALGORITHM Population characteristics Population size 50 Parameters controlling diversity Maximum number of generations 1000 Stepsize F from interval [0, 2] 0.8 Number of variables 2 crossover probability 0.5 Xvmin -2 Strategy 2 Xvmax 2 Convergence criteria 1.00E-08 Initial seed 30
Ploting options
Modified DEMinimum fitness improvement MFI 0.080%
EXPERIMENTATION Number of Generations control (N) 20 Parameter to explore Seed population increase control (times) 2 Increment 1 Population injections 4 Experiments 2
Function evaluations 2700 Maximum 7.390926E-09
seed Objective
30 3400 4.836841E-0931 2700 7.390926E-09
function evaluations
29.8 30 30.2 30.4 30.6 30.8 31 31.20
500
1000
1500
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4000
EXPERIMENT RESULTS
seed
Fu
nc
tio
n E
va
lua
tio
ns
Plot the best from each generation
Parameters Required in Experimentation Option
Parameters Required in Injection Option
29.8 30 30.2 30.4 30.6 30.8 31 31.20
500
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3500
4000
EXPERIMENT RESULTS
seed
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lua
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SIMULATED ANNEALING ALGORITHM General Information Number of parameters 2 Cooling schedule Step size 1 Initial temperature 50 Iterations at each temperature 5 Temperature reduction 0.5 Cycles per iteration 20 Minimum parameter value 4 Maximum parameter value 6 Initial seed 30 Total function evaluations 20000 Stopping criteria (repetitions) 8
EXPERIMENTATION Parameter to explore Seed Increment 5 Ploting options Experiments 3
Function evaluations 3801 Minimum -1
seed Evaluations Objective30 3401 -135 2401 -140 3801 -1
28 30 32 34 36 38 40 420
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1000
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EXPERIMENT RESULTS
seed
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ncti
on
Evalu
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on
s
Plot the best from each generation
Parameters Required in Experimentation Option
28 30 32 34 36 38 40 420
500
1000
1500
2000
2500
3000
3500
4000
EXPERIMENT RESULTS
seed
Fu
ncti
on
Evalu
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Generation Average fitness Maximum Function evals Best fitness Best individual1 0.054080139846 0.83644926548 1500 0.999073982239 0.0663472414022 0.100395649672 0.990944802761 0.0678121298553 0.07964450866 0.9909448027614 0.087495900691 0.9909448027615 0.081682011485 0.9909448027616 0.079100884497 0.9909448027617 0.102888189256 0.9909448027618 0.105679087341 0.9909448027619 0.124293647707 0.990944802761
10 0.173792257905 0.99094480276111 0.17962603271 0.99094480276112 0.197637811303 0.99094480276113 0.099046200514 0.99094480276114 0.103207968175 0.99874132871615 0.125505045056 0.99874132871616 0.154545083642 0.99874132871617 0.160544261336 0.99874132871618 0.082174286246 0.99874132871619 0.136733114719 0.99874132871620 0.104014746845 0.99874132871621 0.12085480243 0.99874132871622 0.084054633975 0.99874132871623 0.135230392218 0.99874132871624 0.192223235965 0.99874132871625 0.127027362585 0.99874132871626 0.137405663729 0.99874132871627 0.164078265429 0.99874132871628 0.146615982056 0.99874132871629 0.196901634336 0.99874132871630 0.139695435762 0.99874132871631 0.150956839323 0.99874132871632 0.142944037914 0.99874132871633 0.201223298907 0.99874132871634 0.221900045872 0.99878543615335 0.184005305171 0.99878543615336 0.090767018497 0.99878543615337 0.072908990085 0.99878543615338 0.059858050197 0.99878543615339 0.120692275465 0.99907398223940 0.073036558926 0.99907398223941 0.090278163552 0.99907398223942 0.114286735654 0.99907398223943 0.146480798721 0.99907398223944 0.245500743389 0.99907398223945 0.202510461211 0.99907398223946 0.114572629333 0.99907398223947 0.143346965313 0.99907398223948 0.15784651041 0.99907398223949 0.115194015205 0.999073982239
50 0.128154024482 0.999073982239
Results for Carroll GA
seed Function Evaluations Objective30 1500 0.99748557806
130 1500 0.998213469982230 1500 0.999073982239
0 50 100 150 200 2500
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EXPERIMENT RESULTS
seed
Fu
nc
tio
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lua
tio
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0 50 100 150 200 2500
200
400
600
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1000
1200
1400
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EXPERIMENT RESULTS
seed
Fu
nc
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lua
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0 200 400 600 800 1000 1200 1400 16000.75
0.8
0.85
0.9
0.95
1
1.05
BEST FITNESS
NUMBER OF FUNCTION EVALUATIONS
FIT
NE
SS