Configuring the Neighborhood Cultivation Genetic Algorithm (NCGA) Technique

In the Neighborhood Cultivation Genetic Algorithm (NCGA) technique, each objective parameter is treated separately. Standard genetic operation of mutation and crossover are performed on the designs. The crossover process is based on the “neighborhood cultivation” mechanism, where the crossover is performed mostly between individuals with values close to one of the objectives. By the end of the optimization run, a pareto set is constructed where each design has the “best” combination of objective values, and improving one objective is impossible without sacrificing one or more of the other objectives.

See Also
Neighborhood Cultivation Genetic Algorithm (NCGA)
Configuring the Technique and the Execution Options
  1. From the Flow section of the action bar, click Optimization and drop it on the process diagram.
  2. Double-click Optimization .
    The Optimization Editor appears.
  3. From the General tab's Optimization Technique list, select NCGA.
  4. In the Optimization Technique Options area, enter or select the following:
    OptionDescription
    Population Size The number of individuals in each generation during execution of the NCGA. The value type is integer. The default value is 10. Other possible values are ≥ 6.
    Number of Generations The number of generations to be analyzed by NCGA. The value type is integer. The default value is 20. Other possible values are ≥ 1.
    Crossover Type The type of crossover operation: one point crossover or two point crossover. The default value is 1. The other possible value is 2.
    Crossover Rate The probability of crossover operation for each individual in every generation during execution of NCGA. The value type is real. The default value is 1.0. Other possible values are ≥ 0⁢⁢ and ≤  1.0.
    Use Optimal Mutation Rate Control whether or not NCGA will use the optimum mutation rate value calculated internally. The default value is false (no).
    Mutation Rate The probability of mutation for each individual. The value type is real. The default value is 0.01. Other possible values are 0<Mutation⁢Rate≤1.0.
    Gene Size The size of the gene used to represent each individual. The value type is integer. The default value is 20. Other possible values are  1⁢ ≤ Gene⁢Size≤ 63.
    Save Technique Log Most optimization techniques create a log file of information/messages as they run. This information can be useful for determining why an optimizer took the path that it did or why it converged. Some of these log files can get extremely large, so they are not stored with the run results by default. Select this option if you want to store the log with the run results (as a document) for later viewing.
    Use initialization file Whether or not the algorithm will use a data file for the initial generation. The default value is false (no).
    Initialization File

    The name of the data file to be used for initial generation. The requirements for the initialization file are:

    • The first line must contain parameter names, separated by a space or tab; the remaining lines must contain data values.
    • Each line must have the same number of values as the number of parameter names in the header line. Only the input values are used from the initialization file; it is not necessary to include output values. All design points read from the initialization file will be sent for evaluation by the Optimization adapter, as if they were randomly generated points.
    • Only the required number of data points will be used from the initialization file. The NCGA technique always includes the starting point sent into the adapter (or set manually in the Parameters table); the remaining initial population will be read by NCGA from the initialization file, if configured to do so. This means that NCGA will read one data point less than the size of the initial population.
    • If your data file contains more designs than necessary, make sure that the needed data points are located at the beginning of the file. If the number of solutions in the initialization file is more than the population size, extra solutions at the end of the file are discarded (not read).
    • The rest of the design points are generated randomly if the data file does not contain enough data points to fill the initial population.

    Iterations for Constraint Violations The number of attempts that NCGA will make to satisfy constraints when any individual in the offspring generation violates constraints. The design, in this case, is gradually moved back to the previous design known to not violate any constraints. The value type is integer. The default value is 0. Other possible values are ≥ 1.
    Maximum Failed Runs The maximum number of failed subflow evaluations that can be tolerated by the optimization technique. If the number of failed runs exceeds this value, the optimization adapter will terminate execution. To disable this feature, set this option to any negative value (for example, –1). When this option is set to a negative value, the optimization will continue execution despite any number of failed subflow runs.
    Failed Run Penalty Value The value of the Penalty parameter that is used for all failed subflow runs. The default value is 1⁢ × ⁢1030.
    Failed Run Objective Value The value of the Objective parameter that is used for all failed subflow runs. The default value is 1⁢ × ⁢1030.
    Use fixed random seed If this option is selected, the random number generator used by the optimization algorithm is seeded using the value specified in the corresponding text box.

    If this option is not selected, the random number generator is seeded by using the clock time at the moment of execution.

    Random seed value All executions of the Optimization adapter will use the same sequence of random numbers and, therefore, will produce the same design points. This arrangement is useful for debugging the optimization process when it is necessary to reproduce the same sequence of design points.
  5. Click Ok to save your changes and to close the Optimization Editor.