8. Optimization of clusters¶
8.1. Theoretical backgrounds¶
Optimization of clusters using the genetic algorithm
8.2. gasa¶
Searching for the set of clusters that minimizes the CV score using the genetic algorithm.
An initial population can be generated using the simulated annealing.
Multi-thread calculations using OPENMP are available only for the simulated annealing.
Input files
GASA.in
Output files
gasa.out
8.3. wgasa¶
Searching for the set of clusters that minimizes the weighted CV score using the genetic algorithm.
Input files
GASA.in
Output files
wgasa.out
8.4. GASA.in¶
Example
The set of clusters is optimized using the genetic algorithm. The cluster set is composed of no.0-5 clusters and 5 clusters selected among no.6-52 clusters.
NLOOP = 0 # only performing genetic algorithm
BASECLUSTER = 0 1 2 3 4 5
NPOP = 25
NELITE = 1
NALLCLUSTER = 53
MININDEX = 6
MAXINDEX = 52
NCLUSTER = 5
MAXGENE = 300
PMATING = 0.9
PMUTATION = 0.03
The initial population is generated by simulated annealing and the set of clusters is optimized using the genetic algorithm. The cluster set is composed of no.0-5 clusters and 5 clusters selected among no.6-52 clusters.
NLOOP = 100
TEMPINIT = 1
TEMPFINAL = 0.001
TEMPMUL = 0.9
BASECLUSTER = 0 1 2 3 4 5
NPOP = 25
NELITE = 1
NALLCLUSTER = 53
MININDEX = 6
MAXINDEX = 52
NCLUSTER = 5
MAXGENE = 300
PMATING = 0.9
PMUTATION = 0.03
8.4.1. NALLCLUSTER tag¶
Number of all clusters included in CORRELATION file.
Default : none
Example : NALLCLUSTER = 53
8.4.2. NCLUSTER tag¶
Number of clusters for selection.
Default : none
Example : NCLUSTER = 5
8.4.3. MININDEX tag, MAXINDEX tag¶
Minimum and maximum cluster index for the optimization.
Default : MININDEX = 0, MAXINDEX = NALLCLUSTER - 1
Example : MININDEX = 5
Example : MAXINDEX = 52
8.4.4. BASECLUSTER tag¶
Cluster indexes included for ECI evaluation.
Default : empty
Example : BASECLUSTER = 0 1 2 3 4 5
8.4.5. EXCLUSTER tag¶
Cluster indexes excluded for the optimization.
Default : empty
Example : EXCLUSTER = 15 23 34
8.4.6. NPOP tag¶
Number of cluster sets included in the population.
Default : 30
Example : NPOP = 25
8.4.7. NELITE tag¶
Number of elites (good cluster sets) kept for the next optimization step.
Default : 1
Example : NELITE = 5
8.4.8. PMATING tag¶
Probability of mating.
Default : 0.9
Example : PMATING = 0.95
8.4.9. PMUTATION tag¶
Probability of mutation.
Default : 0.03
Example : PMUTATION = 0.05
8.4.10. NLOOP tag¶
Number of steps for simulated annealing at one temperature. If you perform only the genetic algorithm, set NLOOP = 0.
Default : 0
Example : NLOOP = 100
8.4.11. TEMPINIT tag, TEMPFINAL tag¶
Initial and final temperatures in the simulated annealing.
Default : none
Example : TEMPINIT = 1
Example : TEMPFINAL = 0.001
8.4.12. TEMPMUL tag¶
Exponential base used in an automatic setting of temperatures in the simulated annealing. Set values ranged from 0 to 1.
Default : none
Example : TEMPMUL = 0.9