Analyzer

Analyzers are HeuristicLab 3.3 operators that implement the IAnalyzer interface. There are a couple of general purpose Analyzers, such as the QualityAnalyzer or the MultiAnalyzer as well as encoding and problem specific Analyzers.

---

1. General

1.1 MultiAnalyzer

An analyzer which applies arbitrary many other analyzers. It is used as a parameter in all currently implemented problems (Evolution Strategy?, Genetic Algorithm? etc.) to ensure easy extendability.

For example, if you open the GA for the ch130 problem sample on the Optimizer Start Page, navigate to the Parameters tab and click on the Analyzer property, you will see the following list of sub-Analyzers:

The BestAverageWorstQualityAnalyzer comes from the genetic algorithm and the BestTSPSolutionAnalyzer was provided by the TSP problem. This wiring also works if you configure your own algorithm in the Optimizer. Opening a new Evolution Strategy algorithm and loading a Griewank test function as problem will result in a BestAverageWorstQualityAnalyzer and a BestSingleObjectiveTestFunctionSolutionAnalyzer. You can of course add additional Analyzers via the GUI.

Operator Parameters:

Parameter	Description
UpdateInterval	The interval in which the contained analyzers should be applied (Default: 1)
UpdateCounter	The value which counts how many times the MultiAnalyzer was called since the last update.

1.2 MinAverageMaxValueAnalyzer

An operator which analyzes the minimum, average and maximum of a value in the scope tree.

1.3 BestAverageWorstQualityAnalyzer

An operator which analyzes the best, average and worst quality of solutions in the scope tree.

1.4 QualityAnalyzer

An operator which analyzes the quality of solutions in the scope tree.

1.5 ValueAnalyzer

An operator which analyzes a value in the scope tree.

---

2. Tabu Search

2.1 TabuNeighborhoodAnalyzer

Operator Parameters:

Parameter	Description
IsTabu	A value that determines if a move is tabu or not.
PercentTabu	Indicates how much of the neighborhood is tabu.
Results	The result collection where the value should be stored.

---

3. Artificial Ant Problem

3.1 BestAntTrailAnalyzer

An operator for analyzing the best ant trail of an Artificial Ant Problem?.

Operator Parameters:

Parameter	Description
BestSolution	The visual representation of the best ant trail.
MaxTimeSteps	The maximal time steps that the artificial ant has available to collect all food items.
Quality	The qualities of the artificial ant solutions which should be visualized.
Results	The result collection where the best artificial ant solution should be stored.
SymbolicExpressionTree	The artificial ant solutions from which the best solution should be visualized.
World	The world with food items for the artificial ant.

---

4. Knapsack Problem

4.1 BestKnapsackSolutionAnalyzer

An operator for analyzing the best solution for a Knapsack Problem?.

Operator Parameters:

Parameter	Description
BestKnownQuality	The quality of the best known solution.
BestKnownSolution	The best known solution.
BestSolution	The best knapsack solution.
BinaryVector	The knapsack solutions from which the best solution should be visualized.
KnapsackCapacity	Capacity of the Knapsack.
Maximization	True if the problem is a maximization problem.
Quality	The qualities of the knapsack solutions which should be analyzed.
Results	The result collection where the knapsack solution should be stored.
Values	The values of the items.
Weights	The weights of the items.

---

5. OneMax Problem

5.1 BestOneMaxSolutionAnalyzer

An operator for analyzing the best solution for a OneMax Problem.

Operator Parameters:

Parameter	Description
BestKnownQuality	The quality of the best known solution.
BestKnownSolution	The best known solution.
BinaryVector	The Onemax solutions from which the best solution should be visualized.
Maximization	True if the problem is a maximization problem.
Quality	The qualities of the Onemax solutions which should be analyzed.
Results	The result collection where the Onemax solution should be stored.

---

6. Single Objective Test Function Problem

6.1 BestSingleObjectiveTestFunctionSolutionAnalyzer

An operator for analyzing the best solution for a Single Objective Test Function problem.

Problem Parameters:

Parameter	Description
BestKnownQuality	The quality of the best known solution of this test function.
BestKnownSolution	The best known solution for this test function instance.
BestSolution	The best SingleObjectiveTestFunction solution.
Evaluator	The evaluator with which the solution is evaluated.
Maximization	Set to false as most test functions are minimization problems.
Quality	The qualities of the test function solutions which should be analzed.
RealVector	The SingleObjectiveTestFunction solutions from which the best solution should be visualized.
Results	The result collection where the SingleObjectiveTestFunction solution should be stored.

---

7. Travelling Salesman Problem

7.1 BestTSPSolutionAnalyzer

An operator for analyzing the best solution of a Travelling Salesman Problem? given in path representation using city coordinates.

Operator Parameters:

Parameter	Description
BestKnownQuality	The quality of the best known solution of this TSP instance.
BestKnownSolution	The best known solution of this TSP instance.
BestSolution	The best TSP solution.
Coordinates	The x- and y-Coordinates of the cities.
Maximization	True if the problem is a maximization problem.
Permutation	The TSP solutions given in path representation from which the best solution should be analyzed.
Quality	The qualities of the TSP solutions which should be analzed.
Results	The result collection where the best TSP solution should be stored.