def evaluate(particle):
# Find all selected features
indices = [index for index, entry in enumerate(particle) if entry == 1.0]
# Build new dataset with selected features
src_feature = Core.Xs[:, indices]
tar_feature = Core.Xt[:, indices]
# return FitnessFunction.fitness_confuse(src_feature, Core.src_label, tar_feature, Core.classifier),
# return FitnessFunction.fitness_function(src_feature=src_feature, src_label=Core.src_label,
# tar_feature=tar_feature, classifier=Core.classifier)[0],
return FitnessFunction.fitness_function_framework(
src_feature=src_feature,
src_label=Core.Ys,
classifier=Core.classifier,
tar_feature=tar_feature)[0],
