alogos._optimization.ea.operators.selection¶
Functions¶
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Perform uniform selection via uniform sampling with replacement. |
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Perform truncation selection via deterministic cut-off. |
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Perform tournament selection via sampling with replacement. |
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Perform rank-proportional selection with linear scaling. |
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Perform fitness-proportional selection with linear scaling. |
Detailed object descriptions¶
- alogos._optimization.ea.operators.selection.uniform(individuals, sample_size, objective, parameters, state)[source]¶
Perform uniform selection via uniform sampling with replacement.
References
Eiben, Introduction to Evolutionary Computing (2e 2015): p. 86
- alogos._optimization.ea.operators.selection.truncation(individuals, sample_size, objective, parameters, state)[source]¶
Perform truncation selection via deterministic cut-off.
Given a population, return the best <proportion> of them.
- alogos._optimization.ea.operators.selection.tournament(individuals, sample_size, objective, parameters, state)[source]¶
Perform tournament selection via sampling with replacement.
Given a population, draw <tournament_size> competitors randomly and select the single best of them.
- alogos._optimization.ea.operators.selection.rank_proportional(individuals, sample_size, objective, parameters, state)[source]¶
Perform rank-proportional selection with linear scaling.
- alogos._optimization.ea.operators.selection.fitness_proportional(individuals, sample_size, objective, parameters, state)[source]¶
Perform fitness-proportional selection with linear scaling.
Considerations for special float values:
NaN values are ignored, i.e. the individual has 0.0% chance of being selected.
+Inf values are 1) ignored in minimization or 2) replaced by a large positive number in maximization.
-Inf values are 1) ignored in maximization or 2) replaced by a large negative number in minimization.