Changes

Optimization can have detrimental effects if the user searches for the combination of inputs based solely on the best performance over a period of historical data and focuses to too much on market conditions that may never occur again. This approach is known as over-optimization or curve-fitting. Performance will not be the same in real trading, since historical patterns are highly unlikely to be repeated.

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:

# Each individual is selected at random. These individuals form the first Generation. The optimal number of individuals is automatically placed in next to the '''Set Population Size''' field box and can be changed manually.  <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><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''' #: 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. #: 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. #: 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. #: 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. #: <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. #: GA subtype can be set in the '''Genetic Algorithm Subtype''' section.#: Two GA subtypes are available: '''Basic''' and '''Incremental'''.#: '''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. #: '''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.<br>#: '''Replacement Schemas''' #: Replacement Schemas are available for Incremental subtype only. Schemas define how a new generation should be integrated into the population. There are three schemes available: '''Worst''', '''Parent''', and '''Random'''.#: '''Worst''' – least fit individuals are replaced #: '''Parent''' – parent individuals are replaced #: '''Random''' – individuals are replaced randomly<br>

# A new population of individuals The process is generated from repeated, until the remaining members specified degree of the previous population by applying the crossover and mutation operations, as well as selection and/convergence or replacement strategies that depend generation number is reached (depends on the GA subtype:  setting selected).<br>#: '''Crossover and MutationGA Convergence Type'''  MultiCharts uses the so#: Genetic Algorithms optimization process has no implicit final result and thus can proceed forever. Therefore, an "ending-called Array Uniform Crossover. With this Crossover typepoint" must be specified, each of indicating when the child’s genes can optimization process must come from each of the parents with equal probabilityto an end. <br>In the #: Two GA optimization "ending-point" criteria types can be selected: '''Crossover ProbabilityTerminate-Upon-Generation''' 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 and '''Mutation ProbabilityTerminate-Upon-Convergence''' 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 SchemasTerminate-Upon-Generation'''  GA subtype defines will stop the way that GA creates new individuals and replaces old individuals when creating next generations. <br>GA subtype can be set in optimization process once the specified '''Genetic Algorithm SubtypeMaximum Number of Generations''' drop-down listis reached.<br>Two GA subtypes are available#: '''BasicTerminate-Upon-Convergence''' and will stop the optimization process once the defined '''IncrementalConvergence Rate'''.<br>'''Basic''' subtype is the standard so-called “simple genetic algorithm”. This algorithm uses non-overlapping generations and Elitism mode (optional). For each generationreached, or once the algorithm creates an entirely new population of individuals (if the defined '''ElitismMaximum Number of Generations''' option is selected, the most fit individuals move on to the next generation)reached.  <br>#: GA optimization "ending-point" criterion is selected in the '''ElitismConversion Type'''section. Elitism mode#: The desired Maximum Number of Generations, available for the Basic GA subtype onlyMinimum Number of Generations, allows and Conversion Rate can be set in the fittest individuals to survive and produce “children” over a span of multiple generationscorresponding text boxes. <br>#: '''IncrementalConvergence Rate''' subtype does not create an entirely new population for each generation. It simply adds only one or #: Convergence Rate of generations is the ratio between the Convergence value of the two children to most recent generations and the Convergence value of the population each time current generation and the next generation N generations ago. #: GA calculation is created. These one or two children replace one or two individuals in stopped after meeting С [x – N] / C [x] >= P condition where: #: x – ordinal number of the previous current generation. The individuals to be replaced by ; #: С[x] – convergence value of the two most recent generations; #: N – defined minimal number of the children generations; #: P - convergence rate; values used are chosen according usually close to 1, with the Replacement Schemas useddefault value of 0.99.  #: <div style="background-color: #E3FBE5;">Note: Convergence Rate is not calculated for generations that have an ordinal number less than the defined minimal number of the generations.</div><br>'''Replacement Schemas'''