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				| @ -91,12 +91,11 @@ class ExperimentAnalysis(EA): | ||||
|             ) | ||||
|             feasible_value = k_values.take(feasible_index) | ||||
|             f_best[k_metric] = np.min(feasible_value) | ||||
| 
 | ||||
|             feasible_index_filter = np.where( | ||||
|                 feasible_value | ||||
|                 <= max( | ||||
|                     [ | ||||
|                         f_best[k_metric] | ||||
|                         + self.lexico_objectives["tolerances"][k_metric] | ||||
|                     f_best[k_metric] + self.lexico_objectives["tolerances"][k_metric] | ||||
|                     if not isinstance( | ||||
|                         self.lexico_objectives["tolerances"][k_metric], str | ||||
|                     ) | ||||
| @ -111,7 +110,6 @@ class ExperimentAnalysis(EA): | ||||
|                         ) | ||||
|                     ), | ||||
|                     k_target, | ||||
|                     ] | ||||
|                 ) | ||||
|             )[0] | ||||
|             feasible_index = feasible_index.take(feasible_index_filter) | ||||
|  | ||||
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