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In general, GA's work is primarily about two abstracts: an Individual (or Genome) and an Algorithm (i.e. Genetic Algorithm itself). Each Genome instance represents a single unique inputs combination, while GA itself defines how the evolution should take place. The GA uses a given trading strategy to determine how 'fit' a genome is for survival, e.g. how much Net Profit does an inputs combination generates in case Net Profit was selected as an Optimization Criteria.
<br>Here are some GA definitions that help in understanding the process:
<br>'''Fitness''' - the overall performance of an individual (e.g. Net Profit).
# After a number of all possible combinations is determined, an optimal number of individuals is selected.
# 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>
# The fitness of each individual is evaluated and the least fit individuals discarded.
# 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'''
