In this paper we propose a differential evolution (DE) based named entity recognition (NER) system in twitter data. In the first step, we develop various NER systems using different combinations of the features. We implemented these features without using any domain-specific features and/or resources. As a base classifier we use Conditional Random Field (CRF). In the second step, we propose a DE based feature selection approach to determine the most relevant set of features and its context information. The optimized feature set applied to the training set yields the precision, recall and F-measure values of 60.68%, 29.65% and 39.84%, respectively for the fine-grained named entity (NE) types. When we consider only the coarse-grained NE types, it shows the precision, recall and F-measure values of 63.43%, 51.44% and 56.81%, respectively.
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