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# Open the '''Format Objects''' window.<br><span>{{FormatObjectIS}}</span>
# Select the '''Signals''' tab.
# Click the '''Optimize''' button.
# In the '''Select Optimization Method''' dialog box that appears, select '''Genetic Algorithm'''.
# In the '''Genetic Algorithm Properties''' window that opens, select the '''Optimizable Inputs''' tab.
# Check/uncheck the check box to the left of the signal name and input name to enable/disable optimization for this input or check the check box to the left of the '''Signal Name''' column heading to enable optimization for all inputs (at least one input should be selected to perform optimization).
# The '''Current Value''' column shows input values that are currently selected for the signals applied on the chart.
# In the '''Start Value''' column, enter the desired starting values for each of the inputs.
# In the '''End Value''' column, enter the desired ending values for each of the inputs.
# In the '''Step''' column, enter the desired step size, for each of the inputs.
# The '''Step Count''' column shows current amount of steps for an input.
# Select the '''Algorithm-specific Properties''' tab.
# Select criteria:
#: 1. Select '''Standard Criteria'''.
#: 2. Select the criteria from the drop-down list. [[Performing_Optimization#Standard Criteria|Read more about Standard Criteria]].<br>or:<br>
#: 1. Select '''Custom Criteria'''.
#: 2. Click '''Edit...''' button.
#: 3. Write the script for the custom criteria. [[Performing_Optimization#Custom Criteria|Read more about Custom Criteria]].
#: 4. Click '''OK'''.
# Select '''Ascending''' or '''Descending''' option to sort the output in ascending or descending order, respectively.
# An optimum population size value is automatically placed into the '''Population Size''' box; if a different value is desired, enter the value into the box.
# In the '''Crossover Probability''' box, enter the desired crossover probability; value range is 0.95-0.99, with a default of 0.95.
# In the '''Mutation Probability''' box, enter the desired mutation probability; value range is 0.01-0.05, with a default of 0.05.
# In the '''Convergence Type''' drop-down list, select '''Number of Generations''' or '''Proximal Convergence'''.
# In the '''Maximum Number of Generations''' box, enter the desired maximum number of generations.
# If Proximal Convergence was selected, enter the desired minimum number of generations and convergence rate into the respective boxes. A value, approaching 1 is usually selected for the convergence rate; the default value is 0.990000.
# In the '''Genetic Algorithm Subtype''' drop-down list, select '''Basic''' or '''Incremental''' algorithm subtype.
# If Basic algorithm subtype was selected, select '''Yes''' or '''No''' for '''Use Elitism''' option.
# If Incremental algorithm subtype was selected, select the '''Replacement Scheme''' and '''Offspring Number''' (number of “children”).
# Click '''OK''' to run the optimization and generate the Optimization Report.
# Optimization dialogue window shows Average fitness value for current population during optimization.
To perform Genetic Algorithm optimization:
:1. Open the '''Format Objects''' window.<br><span>{{FormatObjectIS}}</span>
:2. Select the '''Signals''' tab.
:3. Click the '''Optimize''' button.
:4. In the '''Choose the Optimization Type''' dialog box that appears, select '''Regular Optimization''' and click '''Next'''.
:5. In the '''Set Optimizable Inputs''' section check/uncheck the check box to the left of the signal name and input name to enable/disable optimization for this input or check the check box to the left of the '''Signal Name''' column heading to enable optimization for all inputs (at least one input should be selected to perform optimization).
:6. The '''Current Value''' column shows input values that are currently selected for the signals applied on the chart.
:7. In the '''Start Value''' column, enter the desired starting values for each of the inputs.
:8. In the '''End Value''' column, enter the desired ending values for each of the inputs.
:9. In the '''Step''' column, enter the desired step size, for each of the inputs.
:10. The '''Step Count''' column shows current number of steps for an input.
:11. Select '''Genetic''' radio button in the '''Set Optimization Method''' section.
:12. Selecting the target function to be optimized in the '''Optimize by:''' section:
In the '''Maximal/Minimal''' list, set the respective individuals, which will take part in further selection and crossover, for the selected parameter.
From the next list one can select the function itself.
<br>If '''Custom Criteria''' is selected, Edit button is available. Press it to write the script for the custom criteria. [[Performing_Optimization#Custom Criteria|Read more about Standard and Custom Criteria]].
<br>In order to apply the desired function from code select '''Custom Fitness Value'''. The custom value shall be specified by [[SetCustomFitnessValue]] keyword in the script.
:13. Set Basic Genetic Algorithm Parameters.
:14. An optimum population size value is automatically placed next to the '''Set Population Size''' box; if a different value is desired, check '''Set Population Size''' box and select the value.
:15. In the '''Crossover Probability''' field, select the desired crossover probability; value range is 0-1, with a default of 0.95.
:16. In the '''Mutation Probability''' field, select the desired mutation probability; value range is 0-1, with a default of 0.05.
:17. In the '''Convergence Type''' section, select '''Number of Generations''' or '''Proximal Convergence'''.
:18. If '''Number of Generations''' is selected:
In the '''Number of Individuals in Population for Crossover''' field, enter the desired number of individuals in population for crossover.
<br>In the '''Maximum Number of Generations''' field, enter the desired maximum number of generations.
<br>
<div style="background-color: #E3FBE5;">Note: the above fields are grayed out if “Set Population Size” box is not checked in Basic Genetic Algorithm Parameters.</div>
:19. If '''Proximal Convergence''' is selected:
In the '''Number of Individuals in Population for Crossover''' field, enter the desired number of individuals in population for crossover.
<br>In the '''Maximum Number of Generations''' field, enter the desired maximum number of generations.
<br>In the '''Minimal Number of Generations''' field, enter the desired minimal number of generations.
<br>
<div style="background-color: #E3FBE5;">Note: the above fields are grayed out if “Set Population Size” box is not checked in Basic Genetic Algorithm Parameters.</div>
:20. If '''Proximal Convergence''' was selected, enter the desired convergence rate into the respective field. A value, approaching 1 is usually selected for the convergence rate; the default value is 0.99.
:21. In the '''Genetic Algorithm Subtype''' section, select '''Basic''' or '''Incremental''' algorithm subtype.
:22. If Basic algorithm subtype was selected, select '''Yes''' or '''No''' for '''Use Elitism''' option.
:23. If Incremental algorithm subtype was selected, select the '''Replacement Scheme''' between Worst, Parent and Random.
:24. Click '''Optimize''' N '''combinations''' to run the optimization and generate the Optimization Report.
:25. Optimization dialogue window shows Average fitness value for current population during optimization.
<br>
<div style="background-color: #E3FBE5;">Since '''MultiCharts 10''' that is possible to start optimization from '''Chart Analysis''' toolbar (see [[MultiCharts_Work_Area#Hiding_and_Redisplaying_Toolbars]]) by clicking on the '''Optimize Strategy''' button: [[File:toolbar2_ChartAn_125.png]]</div>
