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wiki:UsersSamples

Version 12 (modified by mkofler, 14 years ago) (diff)

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Samples

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


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: Travelling Salesman Problem

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
  • DistanceMatrix: null
  • Evaluator: !TSPRoundedEuclideanPathEvaluator
  • Maximization: False
  • SolutionCreator: RandomPermutationCreator
  • UseDistanceMatrix: True

Genetic programming for artificial ant problem

GP Result for SantaFe Ant Trial

Algorithm: Genetic Algorithm

Algorithm Parameters:

  • Analyzer: MultiAnalyzer
  • Crossover: SubtreeCrossover - An operator which performs subtree swapping crossover
  • Elites: 1
  • MaximumGenerations: 100
  • MutationProbability: 15%
  • Mutator: MultiSymbolicExpressionTreeManipulator
  • Population Size: 500
  • Seed: -
  • Selector: TournamentSelector
  • SetSeedRandomly: True

Problem: Artificial Ant Problem

Problem Parameters:

  • ArtificialAntExpressionGrammar: IfFoodAhead, Prog2, Prog3, Right, Left, Move
  • BestKnownQuality: 89
  • Evaluator: ArtificialAntEvaluator
  • MaxExpressionDepth: 6
  • MaxExpressionLength: 50
  • MaxFunctionArguments: 3
  • MaxFunctionDefinitions: 3
  • Maximization: True
  • MaxTimeSteps: 600
  • SolutionCreator: ProbabilisticTreeCreator
  • World: 32x32 grid, 89 randomly scattered food items

Genetic programming for symbolic regression

Algorithm: Genetic Algorithm

Algorithm Parameters:

Problem: Symbolic Regression Problem

Problem Parameters:

  • BestKnownQuality: null
  • DataAnalysisProblemData: Data imported from Housing Dataset from UCI Repository (cf. http://archive.ics.uci.edu/ml/datasets/Housing)
  • Evaluator: SymbolicRegressionScaledMeanSquaredErrorEvaluator
  • FunctionTreeGrammar: Addition, Subtraction, Multiplication, Division, Constant, Variable
  • LowerEstimationLimit: -289,08968253968254
  • MaxExpressionDepth: 10
  • MaxExpressionLength: 100
  • MaxFunctionArguments: 0
  • MaxFunctionDefiningBranches: 0
  • Maximization: False
  • SolutionCreator: ProbabilisticTreeCreator
  • SymbolicExpressionTreeInterpreter: -
  • UpperEstimationLimit: 332,91031746031746

Island Genentic Algorithm - TSP

Algorithm: Island Genetic Algorithm

Algorithm Parameters:

  • Analyzer: MultiAnalyzer
  • Crossover: !OrderCrossover2
  • Elites: 1
  • EmigrantsSelector: BestSelector
  • ImmigrationReplacer: WorstReplacer
  • IslandAnalyzer: MultiAnalyzer
  • MaximumGenerations: 1000
  • MigrationInterval: 50
  • MigrationRate: 25%
  • Migrator: UnidirectionalRingMigrator
  • MutationProbability: 5%
  • Mutator: InversionManipulator
  • NumberOfIslands: 5
  • PopulationSize: 100
  • Seed: -
  • Selector: ProportionalSelector
  • SetSeedRandomly: True

Problem: Travelling Salesman Problem

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
  • DistanceMatrix: null
  • Evaluator: !TSPRoundedEuclideanPathEvaluator
  • Maximization: False
  • SolutionCreator: RandomPermutationCreator
  • UseDistanceMatrix: True

Local Search - Knapsack

Algorithm: Local Search

Algorithm Parameters:

  • Analyzer: MultiAnalyzer
  • MaximumIterations: 1000
  • MoveEvaluator: KnapsackOneBitflipMoveEvaluator
  • MoveGenerator: ExhaustiveBitflipMoveGenerator
  • MoveMaker: OneBitflipMoveMaker
  • SampleSize: 100
  • Seed: -
  • SetSeedRandomly: True

Problem: Knapsack Problem

Problem Parameters:

  • BestKnownQuality: 226
  • BestKnownSolution: Binary Vector
  • Evaluator: KnapsackEvaluator
  • KnapsackCapacity: 134
  • Maximization: True
  • Penalty: 1
  • SolutionCreator: RandomBinaryVectorCreator
  • Values: The values of the items.
  • Weights: The weights of the items.

Simulated Annealing - Rastrigin

Algorithm: Simulated Annealing

Algorithm Parameters:

  • Analyzer: MultiAnalyzer
  • AnnealingOperator: ExponentialDiscreteDoubleValueModifier
  • EndTemperature: 1E-06
  • InnerIterations: 50
  • MaximumIterations: 1000
  • MoveEvaluator: RastriginAdditiveMoveEvaluator
  • MoveGenerator: StochasticNormalMultiMoveGenerator
  • MoveMaker: AdditiveMoveMaker
  • Seed: -
  • SetSeedRandomly: True
  • StartTemperature: 1

Problem: Single Objective Test Function

Problem Parameters:

  • BestKnownQuality: 0
  • BestKnownSolution: [0;0]
  • Bounds: ([-5, 12], [-5,12])
  • Evaluator: RastriginEvaluator
  • Maximization: False
  • ProblemSize: 2
  • SolutionCreator: UniformRandomRealVectorCreator

Tabu Search - TSP

Algorithm: Tabu Search

Algorithm Parameters:

  • Analyzer: MultiAnalyzer
  • MaximumIterations: 1000
  • MoveEvaluator: TSPInversionMoveRoundedEuclideanPathEvaluator
  • MoveGenerator: StochasticInversionMultiMoveGenerator
  • MoveMaker: InversionMoveMaker
  • SampleSize: 500
  • Seed: -
  • SetSeedRandomly: True
  • TabuChecker: InversionMoveSoftTabuCriterion
  • TabuMaker: InversionMoveTabuMaker
  • TabuTenure: 60

Problem: Travelling Salesman Problem

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
  • DistanceMatrix: null
  • Evaluator: TSPRoundedEuclideanPathEvaluator
  • Maximization: False
  • SolutionCreator: RandomPermutationCreator
  • UseDistanceMatrix: True

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