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)
• Island Genetic Algorithm - TSP: An island genetic algorithm which solves the "ch130" traveling salesman problem (imported from TSPLIB)
• Local Search - Knapsack: A local search algorithm that solves a randomly generated Knapsack problem
• Simulated Annealing - Rastrigin: A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function
• Tabu Search - TSP: A tabu search algorithm that solves the "ch130" TSP (imported from TSPLIB)

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

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

Problem: Knapsack

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

A description will follow shortly

Tabu Search - TSP

A description will follow shortly