Samples

This section provides complementary material for the samples that are shipped with the HeuristicLab 3.3 Optimizer.

• Evolution Strategy - Griewank: An evolution strategy which solves the 10-dimensional Griewank test function
• Genetic Algorithm - TSP: A genetic algorithm which solves rge "ch130" travelling salesman problem (imported from TSPLIB)
• Genetic programming for artificial ant problem: A standard genetic programming algorithm for the artificial ant problem (Santa-Fe ant trail)
• Genetic programming for symbolic regression: A standard genetic programming algorithm to solve a symbolic regression problem (Boston Housing dataset)

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

Genetic Algorithm - TSP

This sample demonstrates how to employ a genetic algorithm to optimize a travelling salesman problem instance, namely "ch130" from the ​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

Genetic programming for artificial ant problem

A description will follow shortly

Genetic programming for symbolic regression

A description will follow shortly