= Offspring Selection Genetic Algorithm (OSGA) =
An offspring selection genetic algorithm developed by (Affenzeller et al. 2009). 

'''Algorithm Parameters:'''
||= Parameter         =||= Description =||
|| Analyzer            || The operator used to analyze each generation. ||
|| !ComparisonFactorLowerBound || The lower bound of the comparison factor (start). ||
|| !ComparisonFactorModifier || The operator used to modify the comparison factor. ||
|| !ComparisonFactorUpperBound || The upper bound of the comparison factor (end). ||
|| Crossover           || The operator used to cross solutions. ||
|| Elites              || The numer of elite solutions which are kept in each generation. ||
|| !MaximumGenerations || The maximum number of generations which should be processed. ||
|| !MaximumSelectionPressure || The maximum selection pressure that terminates the algorithm. ||
|| !MutationProbability || The probability that the mutation operator is applied on a solution. ||
|| Mutator             || The operator used to mutate solutions. ||
|| !OffspringSelectionBeforeMutation || True if the offspring selection step should be applied before mutation, false if it should be applied after mutation. ||
|| Population Size     || The size of the population of solutions. ||
|| Seed                || The random seed used to initialize the new pseudo random number generator. ||
|| !SelectedParents     || How much parents should be selected each time the offspring selection step is performed until the population is filled. ||
|| Selector            || !ProportionalSelector: The operator used to select solutions for reproduction. ||
|| !SetSeedRandomly    || True if the random seed should be set to a random value, otherwise false. ||
|| !SuccessRatio        || The ratio of successful to total children that should be achieved. || 

'''Successful offspring analysis'''

If you are interested, what operators perform well in your algorithm you can do an analysis of the successful offspring.

The following steps are required:
- Choose a !MultiOperator (e.g. !MultiCrossover), set its !TraceSelectedOperator parameter to true and name the !SelectedOperator parameter accordingly (e.g. !SelectedCrossover)
- Enable the !SuccessfulOffspringAnalyzer and add the according !StringValue to the !CollectedValues (e.g. !SelectedCrossover)
- The analysis will be added to the results collection of the algorithm

'''Is there a sample/tutorial?'''

Not yet, but we might add one in the future if there is demand. Meanwhile, have a look at our other samples shipped with HeuristicLab [[UsersSamples| here]]. 

'''References:'''
* Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press.