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Samples
- Travelling Salesman
- Real-valued Test Functions
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Genetic Programming
- Genetic programming - Artificial Ant Problem
- Genetic programming - Symbolic Regression (Boston Housing)
- Genetic programming - Symbolic Regression (Tower)
- Genetic programming - Symbolic Regression (Mackey-Glass)
- Genetic programming - Symbolic Classification (Wisconsin)
- Genetic programming - Symbolic Classification (Mammography)
- Genetic Programming - Even Parity (4 inputs)
- Genetic Programming - Multiplexer (11 inputs)
- Additional
Samples
This section provides additional sample files for the HeuristicLab 3.3 Optimizer some of which are included as demo samples in the Optimizer.
Travelling Salesman
- Genetic Algorithm - TSP: A genetic algorithm which solves rge "ch130" travelling salesman problem (imported from TSPLIB)
- Island Genetic Algorithm - TSP: An island genetic algorithm which solves the "ch130" traveling salesman problem (imported from TSPLIB)
- Tabu Search - TSP: A tabu search algorithm that solves the "ch130" TSP (imported from TSPLIB)
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
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
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
Real-valued Test Functions
- Evolution Strategy - Griewank: An evolution strategy which solves the 10-dimensional Griewank test function
- Simulated Annealing - Rastrigin: A simulated annealing algorithm that solves the 2-dimensional Rastrigin test function
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
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
Genetic Programming
- Genetic Programming - Artificial Ant Problem: A standard genetic programming algorithm to solve an artificial ant problem (Santa-Fe ant trail)
- Genetic Programming - Symbolic Regression (Boston Housing): A standard genetic programming algorithm to solve a symbolic regression problem (Boston Housing dataset)
- Genetic Programming - Symbolic Regression (Tower): A genetic programming algorithm to solve a symbolic regression problem (Tower dataset)
- Genetic Programming - Symbolic Regression (Mackey-Glass): A genetic programming algorithm to create a one-step prediction model for a chaotic time series (Mackey-Glass time series)
- Genetic Programming - Symbolic Classification (Wisconsin): A genetic programming algorithm to solve a symbolic classification problem (Wisconsin Diagnostic Breast Cancer dataset)
- Genetic Programming - Symbolic Classification (Mammography): A genetic programming algorithm to solve a symbolic classification problem (Mammography dataset)
- Genetic Programming - Even Parity (4 inputs): A genetic programming algorithm to synthesize the boolean even parity function with 4 binary inputs
- Genetic Programming - Multiplexer (11 inputs): A genetic programming algorithm to synthesize the boolean multiplexer function with 11 binary inputs
Genetic programming - 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 - Symbolic Regression (Boston Housing)
GP_Regression-Boston-Housing.hl
Example for a simple genetic programming algorithm to create a regression model for the estimation of the median value of houses in a certain in the Boston area based on other parameters of that region. The original dataset was downloaded from http://archive.ics.uci.edu/ml/datasets/Housing.
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
Genetic programming - Symbolic Regression (Tower)
GP_Regression-TowerResponse.hl
Example for a simple genetic programming algorithm to create a regression model for the estimation of a product quality parameter in an industrial chemical process. The original dataset was downloaded from http://vanillamodeling.com/realproblems.html.
- Population size: 500
- Generations: 100
- Tournament selection: group size=7
- Mutation rate: 15%
- Function set: +, *, -, /, average, log, power, root, exponential
- Terminal set: constants, x01, x02, x03, x04, x05, x06, x08, x09, x10, x12, x14, x15, x17, x18, x19, x20, x22, x23, x24
- Fitness function: R² of predicted and original tower response
Genetic programming - Symbolic Regression (Mackey-Glass)
Example for a simple genetic programming algorithm to create a dynamic model for the one-step prediction of the chaotic Mackey Glass (T=17) time series. The original dataset was downloaded from http://www.bme.ogi.edu/~ericwan/data.html.
- Population size: 500
- Generations: 100
- Tournament selection: group size=7
- Mutation rate: 15%
- Function set: +, *, -, /, average, log, power, root, exponential
- Terminal set: constants, x(t-90) .. x(t-1)
- Fitness function: R²(xPred(t), xOrig(t))
Genetic programming - Symbolic Classification (Wisconsin)
GP_Classification-WisconsinDiagnostic.hl
Example for a simple genetic programming algorithm to create a classification model for the estimation of malignant or benign tumor diagnosis based on features extracted through analysis of tumorous cells in a tissue sample. The original dataset was downloaded from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)). The algorithm uses all available GP manipulation operators, single-point cross-over and squared Pearson's correlation coefficient evaluation.
The algorithm reaches an accuracy of ~95% on the test set. This algorithm is supposed to be a demo for GP in HeuristicLab, the aim is not to find high quality models.
Genetic programming - Symbolic Classification (Mammography)
GP_Classification-Mammography.hl
A genetic programming algorithm to create a classification model for the prediction of malignant or benign tumor diagnosis based on features extracted through a non-invasive mammography breast cancer screening. Original dataset stems from the UCI Machine Learning Repository (http://archive.ics.uci.edu/ml/datasets/Mammographic+Mass).
Genetic Programming - Even Parity (4 inputs)
A genetic programming algorithm to synthesize the boolean even parity function with 4 binary inputs
- Population size: 4000
- Generations: 100
- Function set: AND, OR, NOT
- Terminal set: D0, D1, D2, D3
- Max. function defining branches: 3
- Max. function arguments: 3
Up to three automatically defined functions (ADF), each with up to three arguments, are allowed.
References
John R. Koza, Genetic Programming - On the Programming of Computers by Means of Natural Selection, MIT Press, 1992
Genetic Programming - Multiplexer (11 inputs)
A genetic programming algorithm to synthesize the boolean multiplexer function with 11 binary inputs
- Population size: 4000
- Generations: 100
- Function set: IF, AND, OR, NOT
- Terminal set: A0, A1, A2, D0, D1, D2, D3, D4, D5, D6, D7
References
John R. Koza, Genetic Programming - On the Programming of Computers by Means of Natural Selection, MIT Press, 1992
Additional
- Local Search - Knapsack: A local search algorithm that solves a randomly generated Knapsack problem
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.
Attachments (7)
-
SantaFe Result.png
(43.7 KB) -
added by mkofler 15 years ago.
GP Result for SantaFe Ant Trial
- GP_Boston-Housing-screenshot.png (72.3 KB) - added by gkronber 14 years ago.
- GP_Mackey-Glass-screenshot.png (94.2 KB) - added by gkronber 14 years ago.
- GP_Mammography-screenshot.png (70.9 KB) - added by gkronber 14 years ago.
- GP_TowerResponse-screenshot.png (67.7 KB) - added by gkronber 14 years ago.
- GP_WisconsinDiagnostic-screenshot.png (55.1 KB) - added by gkronber 14 years ago.
-
PSO_Schwefel-screenshot.png
(58.9 KB) -
added by mkofler 14 years ago.
Screenshot of running PSO sample for Schwefel test function
Download all attachments as: .zip
