**2. The description of the medical objective in gastric cancer surgery**

Gastric cancer patients are mostly cured by operating on them. Different types of surgery are taken into account. Two of them, namely, the partial resection surgery contra the radical surgery are considered by surgeons when evaluating biological markers in the context of their deviations from normal values (Do-Kyong Kim et al., 2009; de Mello et al., 1983).

Nevertheless, a surgeon often must decide if any operation on a patient is possible. The choice between the status "operate" and "do not operate" will constitute the main problem to solve by engaging different algorithms with their origins in Computational Intelligence. The selection will be made on the basis of three biological markers listed as *X* = *age*, *Y* = *CRP-value*  (C reactive proteins), and *Z* = *body weight* (Do-Kyong Kim et al., 2009; de Mello et al., 1983). These are considered as the most important indices in gastric cancer surgery decision making.

As a leading method, which should provide us with decisions "operate" against "do not operate", we adapt the NS (Negative Selection) algorithm of immunological computation. To comprehend better some associations between the body immunological system and artificially invented algorithms based on the body protection system let us recall the most essential definitions of immunity.

Immunity refers to the condition, in which the organism can resist diseases. A broader definition of immunity is a reaction to foreign substances (pathogens). The biological immune system (BIS) has the ability to detect foreign substances and to respond them. One of the main capabilities of the immune system is to distinguish own body cells from foreign substances, which is called self/non-self discrimination (Dasgupta & Nino, 2008; Engelbrecht, 2007; Forrest et al., 1997).

This particular ability is assigned to a special kind of lymphocytes called T-cells produced in the bone marrow. The T-cells can differentiate own body cells from pathogenic cells; therefore they play the role of detectors. Both own cells belonging to the self region and foreign pathogen cells forming non-self domain have their special characteristics given in the form of vectors of coded or measured properties.

Let us adapt the meaning of distribution into self and non-self in the medical application sketched as follows. To make a decision concerning an individual patient we assign the immunological region of self to "operate", whereas the non-self field will be identified with "do not operate" (Rakus-Andersson, 2011).

To be able to use the self/non-self discrimination, accomplished by the NS algorithm, we need to create vectors of coded patient data. The own fuzzy technique will be involved to divide reference sets of *X*, *Y* and *Z* into subintervals assisting growth levels of these biological indices. To the subintervals, in turn, the codes are added. We will arrange a code vector assisting the patient's features after examining his/her values of *X*, *Y* and *Z*.

When implementing the NS algorithm we assume that vectors characteristic of self region are available as input data. We do not intend to test too many casual vectors to decide their similarity with self vectors since we want to use the NS algorithm in the effective way. We thus try to generate the strongest population of strings being representatives of the self region.

In order to select this population we provide another own algorithm that converts the code vector to a real value. This value will be recognized as "characteristics of the patient".
