Changes

# Each individual is selected at random. These individuals form the first Generation. The optimal number of individuals is automatically placed in '''Population Size''' field and can be changed manually.<br><br><div style="background-color: #E5F6FF;">Tip: An excessively large Population Size value will result in an increase in calculation time, while an overly small Population Size value will result in a decrease in calculation accuracy.</div><br><br><div style="background-color: #E3FBE5;">Note: MultiCharts' GA support artificially exclusive population. This means that identical individuals cannot exist inside the same population, and thus the population size can not exceed the total number of input combinations. The population size is constant for each generation.</div>

# A new population of individuals is generated from the remaining members of the previous population by applying the crossover and mutation operations, as well as selection and/or replacement strategies that depend on the GA subtype: <br>#: '''Crossover and Mutation'''  #: MultiCharts uses the so-called Array Uniform Crossover. With this Crossover type, each of the child’s genes can come from each of the parents with equal probability. <br>#: In the '''Crossover Probability''' field, the probability of a crossover for each individual is specified; the usual value range is 0.95-0.99, with the default value of 0.95. <br>#: MultiCharts uses the so-called Random Flip Mutation. With this Mutation type, each gene can be replaced with any other possible gene on random basis. <br>#: In the '''Mutation Probability''' field, the probability of a mutation for each individual is specified; the usual value range is 0.01-0.05, with the default value of 0.05. <br>#: <div style="background-color: #E5F6FF;">Tip: An excessively large Mutation Probability value will cause the search to become a primitive random search.</div> <br>#: '''GA Subtypes and Replacement Schemas'''  #: GA subtype defines the way that GA creates new individuals and replaces old individuals when creating next generations. <br>#: GA subtype can be set in the '''Genetic Algorithm Subtype''' drop-down list.<br>#: Two GA subtypes are available: '''Basic''' and '''Incremental'''.<br>#: '''Basic''' subtype is the standard so-called “simple genetic algorithm”. This algorithm uses non-overlapping generations and Elitism mode (optional). For each generation, the algorithm creates an entirely new population of individuals (if the '''Elitism''' option is selected, the most fit individuals move on to the next generation).  <br>#: '''Elitism''' #: Elitism mode, available for the Basic GA subtype only, allows the fittest individuals to survive and produce “children” over a span of multiple generations. <br>#: '''Incremental''' subtype does not create an entirely new population for each generation. It simply adds only one or two children to the population each time the next generation is created. These one or two children replace one or two individuals in the previous generation. The individuals to be replaced by the children are chosen according to the Replacement Schemas used.