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EvolutionaryConfigsGenAlg
This class is a child of ConfigsGenAlg. It uses an evolutionary algorithm to optimize the generation of group configurations. Note: still in development
EvolutionaryConfigsGenAlg(numberOfConfigChoices, minNumberOfPlayersPerGroup, maxNumberOfPlayersPerGroup, preferredNumberOfPlayersPerGroup, fitnessWeights, qualityWeights: PlayerState, regAlg: RegressionAlg, numMutations: int, numFitSurvivors: int, probOfMutation: float)| Name: expected type | Default value | Description |
|---|---|---|
| regAlg: RegressionAlg | - | The regression algorithm used to predict new states from the existing ones |
| qualityWeights: PlayerCharacteristics | PlayerCharacteristics(ability=0.5, engagement=0.5) | Weights to consider when calculating the quality of predicted group states |
| numMutations: int | - | The number of allowed mutations whenever the mutation operator is selected | | numFitSurvivors: int | The number of allowed survivors between iterations | | probOfMutation: float | The probability of selecting the mutation operator |
initPopulation(): voidInternal method used to initialize the population (create random group configurations and profiles).
fitnessSort(): voidInternal method used to sort the individuals of the population (group configurations and their profiles).
Adaptation
Group Configuration Generation
- ConfigsGenAlg
- RandomConfigsGenAlg
- PureRandomSearchConfigsGenAlg
- EvolutionaryConfigsGenAlg
- ODPIPConfigsGenAlg (exact)
- CLinkConfigsGenAlg (legacy)
Preferences Estimation
Quality Evaluation Algorithms
- QualityEvalAlg
- Group-Based Quality Evaluation:
- Regression-Based Quality Evaluation:
- Tabular Quality Evaluation:
Auxiliary Structures
- InteractionsProfile
- PlayerCharacteristics
- PlayerState
- Personality (Inherent Preference):
- PlayerStatesDataFrame
Model Bridges
Player Data Trim