[[PageOutline]]
= Samples =
This section provides complementary material for the samples that are shipped with the HeuristicLab 3.3 Optimizer. 

 * [#ESGriewank Evolution Strategy - Griewank]: An evolution strategy which solves the 10-dimensional Griewank test function
 * [#GATSP Genetic Algorithm - TSP]: A genetic algorithm which solves rge "ch130" travelling salesman problem (imported from TSPLIB)
 * [#GPAA Genetic programming for artificial ant problem]: A standard genetic programming algorithm for the artificial ant problem (Santa-Fe ant trail)
 * [#GPSR Genetic programming for symbolic regression]: A standard genetic programming algorithm to solve a symbolic regression problem (Boston Housing dataset)
 * [#IslandGA Island Genetic Algorithm - TSP]: An island genetic algorithm which solves the "ch130" traveling salesman problem (imported from TSPLIB)
 * [#LSKnapsack Local Search - Knapsack]: A local search algorithm that solves a randomly generated Knapsack problem
 * [#SARastrigin Simulated Annealing - Rastrigin]: A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function
 * [#TSTSP Tabu Search - TSP]: A tabu search algorithm that solves the "ch130" TSP (imported from TSPLIB)

[=#ESGriewank]
== Evolution Strategy - Griewank == 
A pre-defined evolution strategy which solves the 10-dimensional [TestFunctions#GriewankFunction Griewank test function]. HeuristicLab 3 provides a set of real valued test functions for benchmarking purposes. For a full overview please go the [TestFunctions Test Functions] wiki page. 

'''Algorithm:''' Evolution Strategy

'''Algorithm Parameters:'''
 * Population Size: 20
 * Children: 500
 * !MaximumGenerations: 200
 * !ParentsPerChild: 5
 * !PlusSelection: False (Comma Selection)
 * Recombinator: !AverageCrossover
 * Mutator: !NormalAllPositionsManipulator

'''Problem:''' Single Objective Test Function

'''Problem Parameters:'''
 * !BestKnownQuality: 0
 * !BestKnownSolution: [0;0;0;0;0;0;0;0;0;0]
 * Bounds: [-600, 600]
 * Evaluator: !GriewankEvaluator
 * Maximization: False
 * !ProblemSize: 10
 * !SolutionCreator: !UniformRandomRealVectorCreator

[=#GATSP]
== Genetic Algorithm - TSP == 
This sample demonstrates how to employ a genetic algorithm to optimize a travelling salesman problem instance, namely "ch130" from the [http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/ TSP Lib]. 

'''Algorithm:''' Genetic Algorithm

'''Algorithm Parameters:'''
 * !PopulationSize: 100
 * Elites: 1
 * !MutationProbability: 5%
 * !MaximumGenerations: 1000
 * Selector: !ProportionalSelector
 * Crossover: Crossover2 (cf. Affenzeller, M. et al. 2009. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. CRC Press. p. 135)
 * Mutator: !InversionManipulator

'''Problem:''' Single Objective Test Function

'''Problem Parameters:'''
 * !BestKnownQuality: 6110
 * !BestKnownSolution: The best known solution of this TSP instance (cf. TSP Lib)
 * Coordinates: The x and y coordinates of the cities
 * Evaluator: !TSPRoundedEuclideanPathEvaluator
 * Maximization: False
 * !SolutionCreator: !RandomPermutationCreator
 * !UseDistanceMatrix: True

[=#GPAA]
== Genetic programming for artificial ant problem ==
[[Image(SantaFe Result.png, width=500, right, margin-right=30, margin-left=30)]]

'''Algorithm:''' Genetic Programming

'''Algorithm Parameters:'''

'''Problem:''' Artificial Ant Problem

'''Problem Parameters:'''

[=#GPSR]
== Genetic programming for symbolic regression ==
''A description will follow shortly''

[=#IslandGA]
== Island Genentic Algorithm - TSP ==
''A description will follow shortly''

[=#LSKnapsack]
== Local Search - Knapsack == 
''A description will follow shortly''

[=#SARastrigin]
== Simulated Annealing - Rastrigin == 
''A description will follow shortly''

[=#TSTSP]
== Tabu Search - TSP == 
''A description will follow shortly''