The real challenge in pattern recognition tasks and machine learning processes is to train a discriminator using labeled data and use it to distinguish between future data points as accurate as possible. However, most of the problems in the real world have numerous data. Thereforeassigning labels to every data points in these problemsare a cumbersome or even impossible matter. Semi-supervised learning is one approach to overcome these types of problems. It uses only a small set of labeled with the company of huge remain and unlabeled data to train the discriminator. In semi-supervised learning, it is very essential that which data is labeled and depend on position of data it effectiveness changes. In this paper, we proposed an evolutionary approach called Artificial Immune System(AIS) to determine which data is better to be labeled to get the high quality data. The experimental results represent the effectiveness of this algorithm in finding these data points.

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