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-called Array Uniform Crossover. With this Crossover type, each of the child’s genes #: Genetic Algorithms optimization process has no implicit final result and thus can come from each of the parents with equal probabilityproceed forever. <br>In the '''Crossover Probability''' fieldTherefore, the probability of a crossover for each individual is specified; the usual value range is 0.95an "ending-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 point" must 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 indicating when the default value of 0.05optimization process must come to an end. <br><div style=#: Two GA optimization "backgroundending-color: #E5F6FF;point">Tipcriteria types can be selected: 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 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 SubtypeTerminate-Upon-Convergence''' drop-down list.<br>Two GA subtypes are available#: '''BasicTerminate-Upon-Generation''' and will stop the optimization process once the specified '''IncrementalMaximum Number of Generations'''is reached.<br>#: '''BasicTerminate-Upon-Convergence''' subtype is will stop 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 optimization process once the defined '''ElitismConvergence Rate''' option is selectedreached, or once the most fit individuals move on to the next generation).  <br>defined '''ElitismMaximum Number of Generations''' Elitism mode, available for the Basic GA subtype only, allows the fittest individuals to survive and produce “children” over a span of multiple generationsis reached. <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 #: GA optimization "ending-point" criterion is created. These one or two children replace one or two individuals selected in the previous generation. The individuals to be replaced by the children are chosen according to the Replacement Schemas used.  <br>'''Replacement SchemasConversion Type''' section. 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'''The desired Maximum Number of Generations, '''Parent'''Minimum Number of Generations, and '''Random'''Conversion Rate can be set in the corresponding text boxes. <br>'''Worst''' – least fit individuals are replaced <br>#: '''ParentConvergence Rate''' – parent individuals are replaced <br>'''Random''' – individuals are replaced randomly <br> '''Offspring Number'''  Offspring Number #: Convergence Rate of generations is the number ratio between the Convergence value of the two most recent generations and the Convergence value of children to be added each time that a new the current generation is created. '''One''' or '''Two''' children can be added. * The fitness of each individual is evaluated and the least fit individuals discardedgeneration N generations ago.* The process #: GA calculation is repeated, until stopped after meeting С [x – N] / C [x] >= P condition where: #: x – ordinal number of the specified degree of convergence or current generation number is reached (depends on GA setting selected).; #: С[x] – convergence value of the two most recent generations; <br>#: N – defined minimal number of the generations; '''GA Convergence Type'''  Genetic Algorithms optimization process has no implicit final result and thus can proceed forever. Therefore, an "ending#: P -point" must be specifiedconvergence rate; values used are usually close to 1, indicating when with the optimization process must come to an enddefault value of 0.99. Two GA optimization #: <div style="endingbackground-pointcolor: #E3FBE5;" criteria types can be selected>Note: '''Terminate-Upon-Generation''' and '''Terminate-Upon-Convergence'''Rate is not calculated for generations that have an ordinal number less than the defined minimal number of the generations.</div><br>