Data CLASSIFICATION is one of the main issues in management science which took into account from different approaches. Artificial intelligence methods are among the most important CLASSIFICATION methods, most of them consider total accuracy function in performance evaluation. Since in imbalanced data sets this function considers the cost of prediction errors as a fix amount, in this research a sensitivity function in used in addition to the accuracy function in order to increase the accuracy in all of the predefined classes. In addition, due to complexity in process of seeking information from decision maker, NSGA II algorithm is used to extract the parameters (Weight vector and cut levels between classes). In each iteration, based on the estimated weight vector and data sets, the algorithm calculate the score of each alternative using Sum Product function and then allocates the alternative to one of the classes, comparing to the estimated cut levels, . Then, using the fitness functions, the estimation class and the actual class will compare by two algorithms and this process will continue since optimizing the parameters. Comparison of the NSGA II and NRGA algorithms show the high efficiency of the proposed algorithm.

EVOLUTIONARY altruism has been a serious problem for EVOLUTIONARY theory from its earliest times at mid-nineteen century. Group selection theory was its solution until 1960s.But at that time it was criticized so that it was no longer the solution to that problem. Since then theories put forward to solve the problem by individual and/or gene selection theories. Kin selection, reciprocal altruism, EVOLUTIONARY game theory, the selfish gene, and indirect reciprocity are the main theories of this sort. Altruism according to these theories is apparent not real. Multi-level selection theory is introduced since 1970s that defends "new" group selection theory. In addition cultural group selection theory has come into the field. Altruism according to these theories is real. I agree with the second type of theories that real altruism has been evolved. But perhaps more in agreement with the first type I see it in the "shadow" of EVOLUTIONARY egoism.

Today, with increase in the volume of machine-oriented medical data, the role of interoperability and integrity has become clear. The goal of this research was to investigate the methods of integrating of Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) in the development process of International CLASSIFICATION of Diseases-11 (ICD-11). This study was a narrative review where electronic databases (PubMed, ScienceDirect) and Google Scholar search engine were used to search using predefined combined terms of "ICD-11 SNOMED", "Mapping Alignment Harmonization". Studies were focused on five categories including basic useful concepts for ontology, the interpretation of the code in a situation rather than interpretation of clinical conditions, situation, common ontology, alignment method, and semantic rules. The results of this study showed the differences between the ontology-based terminology and the CLASSIFICATION, making mapping challenging. Meanwhile, Semantic Web techniques provide advanced solutions for the matching and integration of heterogeneous data.

Innovations, commercialized by new or old established firms, located at the core of industrial renewal process. The innovation concept has suffered transformations, along with the evolution of the models that try to explain and understand the innovation process. The innovative process corresponds to all activities that generate technological changes and the dynamic interaction between them, not necessarily being novelties. Linier model, Chain-Linked Model and National Innovation Systems (NIS) Approach, are three models that have developed for innovation process. Innovation process can be viewed as EVOLUTIONARY process. One can recognize some mechanism for innovation evolution. These are grouped into two classes; those that increase configurations variation and those that decrease it. Emergence of knowledge, knowledge flow and recombination are the mechanism to increase variation of configuration. Internal and external selections are the mechanism to selecting. Innovation operators are EVOLUTIONARY operators that create new combinations of configuration and increase variation. This paper develops an EVOLUTIONARY cycle in innovation process and extends EVOLUTIONARY mechanisms of innovation.