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Test Data (1304_100)
magnific0 edited this page Feb 25, 2014
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1 revision
Trials: 200 - Population size: 100 - Generations: 500
Testing problem: Schwefel, Dimension: 10
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 7.54880602472e-11
Mean: 321.811491787
Std: 149.497795999
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 37.3080754035
Std: 62.1970431333
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.000418741766225
Mean: 0.00408167131487
Std: 0.00397321528762
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 2.50111042988e-13
Std: 4.06102369641e-13
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 1.027729013e-10
Mean: 2.55098029811e-08
Std: 4.54618285269e-08
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0148846875791
Mean: 311.943741493
Std: 157.176618944
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 1.08503882075e-05
Mean: 4.22377737823e-05
Std: 1.23880563967e-05
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 1.26712540211
Mean: 326.44794538
Std: 160.888156007
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 1112.15970202
Mean: 1889.17565138
Std: 186.698317457
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.000526080541931
Mean: 36.8042855342
Std: 51.4975625779
Testing problem: Rastrigin, Dimension: 10
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 1.77998221318e-06
Mean: 3.30978722791
Std: 1.32303914191
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.378601807452
Std: 0.648875648102
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 1.66837658038
Mean: 4.84145344636
Std: 1.11337767634
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 1.37845290737e-14
Std: 2.15944213018e-14
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.0
Std: 0.0
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.042757974748
Mean: 4.79070223931
Std: 2.03048248735
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 1.92109023089e-06
Mean: 6.96772759973e-06
Std: 1.93749262297e-06
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.0329712858183
Mean: 0.317640058696
Std: 0.20085197689
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 0.0
Mean: 1.71630437349
Std: 1.29320757925
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 6.06849579299e-06
Mean: 0.00465416322366
Std: 0.0182404228338
Testing problem: Rosenbrock, Dimension: 10
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 0.00388205920609
Mean: 2.68586866182
Std: 1.67491154206
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 4.63994440731
Mean: 6.96597676491
Std: 0.652696338474
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.383225422912
Mean: 1.01731516142
Std: 0.286915426523
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 0.000634143935541
Mean: 2.26227800873
Std: 1.22300025615
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 3.82497190988
Mean: 5.37786392818
Std: 0.352769779198
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.013993005063
Mean: 1.23992841989
Std: 1.90675451222
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 6.27658147253
Mean: 7.76823112234
Std: 4.2408617265
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 4.62596461078
Mean: 34.9602836763
Std: 30.2789469411
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 2.40023256365e-28
Mean: 8.40778244954e-28
Std: 2.8636272212e-28
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 0.119080278201
Mean: 0.593415829342
Std: 0.296528735041
Testing problem: Ackley, Dimension: 10
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 2.30845604854e-08
Mean: 1.47291553851e-07
Std: 7.6814647671e-08
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 4.4408920985e-16
Mean: 3.92574861507e-15
Std: 4.97379915032e-16
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.000180255719315
Mean: 0.000409887004384
Std: 9.53969182846e-05
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 3.05892644548e-10
Mean: 7.98013601866e-10
Std: 3.038410091e-10
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 3.99680288865e-15
Mean: 9.57545154279e-13
Std: 2.55873505507e-12
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.0268173810491
Mean: 0.0788605303661
Std: 0.0233144996014
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 0.000599326779988
Mean: 0.00103274669599
Std: 0.000153789968445
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.0652957216633
Mean: 0.29796835903
Std: 0.122480051694
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 3.99680288865e-15
Mean: 7.86926079854e-15
Std: 1.22410965396e-14
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 5.58761520755e-05
Mean: 0.000408125203223
Std: 0.000187188488298
Testing problem: Griewank, Dimension: 10
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 5.01239161288e-10
Mean: 0.0252894176635
Std: 0.0129794353712
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 0.0064363301188
Std: 0.0085479159542
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 0.0891237899173
Mean: 0.195186789825
Std: 0.0401196786661
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 5.0625692527e-10
Mean: 0.000341176168633
Std: 0.000518352807703
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 0.0
Mean: 7.97452937018e-12
Std: 9.3496263471e-11
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 0.109359663285
Mean: 0.354370479669
Std: 0.142393526688
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 3.58120801203e-05
Mean: 0.00297524468054
Std: 0.00479545008211
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 0.263204096665
Mean: 0.764165024744
Std: 0.237084901777
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 0.0
Mean: 3.69802016708e-05
Std: 0.000521669941442
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 3.2908365194e-06
Mean: 0.00522957782791
Std: 0.00606297404863
Testing problem: Levy5, Dimension: 10
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: -4379.89809629
Mean: -4057.76370448
Std: 213.672935726
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: -4411.52297573
Mean: -4359.33483418
Std: 56.9223085428
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: -3619.50819751
Mean: -2859.02610268
Std: 251.33429022
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: -4411.51416289
Mean: -4392.98901524
Std: 19.0358065612
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: -4405.309108
Mean: -4360.54913116
Std: 30.7026096825
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: -4237.66634321
Mean: -3554.13402897
Std: 391.693292649
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: -4411.49665345
Mean: -4397.66257815
Std: 78.2532028293
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: -4271.03047841
Mean: -3782.84949098
Std: 387.348338872
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: -4411.52297573
Mean: -3847.70505637
Std: 427.553784645
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: -4347.99007423
Mean: -4209.96103867
Std: 67.6308511784
Testing problem: Cassini 1, Dimension: 6
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 4.98904851392
Mean: 8.81716697781
Std: 2.81740974604
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 4.93070825012
Mean: 10.7052506089
Std: 3.32738947635
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 5.02894408973
Mean: 5.50062754835
Std: 1.04821230501
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 5.3042865681
Mean: 5.91197690894
Std: 1.41976886211
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 5.57386291515
Mean: 7.08391864362
Std: 1.25395149253
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 5.03895955159
Mean: 16.7061219557
Std: 7.66808931924
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 5.32330728708
Mean: 6.23305778799
Std: 2.41172056656
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 5.40277958837
Mean: 15.3051027145
Std: 5.16799744722
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 10.9964850603
Mean: 16.027073343
Std: 1.61659361052
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 5.81329252665
Mean: 9.62162069735
Std: 2.16573822545
Testing problem: GTOC_1, Dimension: 8
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: -1218968.05782
Mean: -760814.358401
Std: 158665.298332
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: -1308920.43964
Mean: -915583.206374
Std: 191488.223884
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: -788989.155759
Mean: -425992.996058
Std: 111548.534188
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: -923837.669852
Mean: -587638.95312
Std: 132721.805642
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: -1045458.1968
Mean: -615378.561249
Std: 122086.711347
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: -912415.88758
Mean: -99071.1875081
Std: 159303.567959
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: -1187011.38853
Mean: -835837.927844
Std: 152665.945813
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: -991352.185504
Mean: -216331.129976
Std: 248721.522081
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: -1201387.32605
Mean: -222300.543779
Std: 235466.115623
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: -1083710.64133
Mean: -539365.360971
Std: 173309.582008
Testing problem: Cassini 2, Dimension: 22
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 9.7072618976
Mean: 18.6795794708
Std: 2.64419385963
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 16.4703921367
Mean: 22.4954309474
Std: 1.68953849355
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 18.8317599859
Mean: 26.5027962411
Std: 2.13596131866
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 13.9611210261
Mean: 22.1153377942
Std: 2.46432928774
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 13.5935593734
Mean: 20.9072173672
Std: 2.3410971827
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 9.01890904161
Mean: 22.1949428202
Std: 6.18338697426
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 14.0160355756
Mean: 23.9410804868
Std: 3.30083444113
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 14.2339738917
Mean: 25.449566762
Std: 4.07214128516
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 14.8348589529
Mean: 19.616525472
Std: 1.78651184683
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 14.4483028843
Mean: 22.5065778663
Std: 2.73996273398
Testing problem: Messenger full, Dimension: 26
With Population Size: 100
Algorithm name: Particle Swarm optimization - gen:500 omega:0.7298 eta1:2.05 eta2:2.05 variant:5 topology:2 topology param.:4
Best: 10.3413049859
Mean: 16.3241530322
Std: 1.92570302936
Algorithm name: MDE_pBX - gen:500 q percentage:0.15 power mean exponent:1.5 ftol:1e-30 xtol:1e-30
Best: 11.1142468589
Mean: 15.9999126575
Std: 1.49283649973
Algorithm name: Differential Evolution - gen:500 F: 0.8 CR: 0.9 variant:2 ftol:1e-30 xtol:1e-30
Best: 17.8616908047
Mean: 25.9924840238
Std: 2.59887816227
Algorithm name: jDE - gen:500 variant:2 self_adaptation:1 memory:0 ftol:1e-30 xtol:1e-30
Best: 13.8098133623
Mean: 22.5357613811
Std: 2.3148942044
Algorithm name: DE - 1220 - gen:500 self_adaptation:1 variants:[1, 2, 3, 4, 5, 6, 7, 8, 9, 10] memory:0 ftol:1e-30 xtol:1e-30
Best: 13.8193723964
Mean: 20.7188900446
Std: 2.46361692974
Algorithm name: Simulated Annealing (Corana's) - iter:50000 Ts:1 Tf:0.01 steps:1 bin_size:20 range:1
Best: 6.3748101942
Mean: 19.0828998462
Std: 5.74275248114
Algorithm name: Improved Harmony Search - iter:50000 phmcr:0.85 ppar_min:0.35 ppar_max:0.99 bw_min:1e-05 bw_max:1
Best: 17.070583064
Mean: 20.8610445792
Std: 2.10876688025
Algorithm name: A Simple Genetic Algorithm - gen:500 CR:0.95 M:0.02 elitism:1 mutation:GAUSSIAN (0.1) selection:ROULETTE crossover:EXPONENTIAL
Best: 12.1958101219
Mean: 22.275315374
Std: 4.72618718906
Algorithm name: CMAES - gen:500 cc:-1 cs:-1 c1:-1 cmu:-1 sigma0:0.5 ftol:1e-30 xtol:1e-30 memory:0
Best: 11.8242214841
Mean: 14.7380528846
Std: 1.4143941991
Algorithm name: Artificial Bee Colony optimization - gen:250 limit:20
Best: 16.941070582
Mean: 24.4110181658
Std: 3.09883252725