such as fuzzy systems and evolutionary computation, which they related to Computational Intelligence in 2011 (Dote and Ovaska). But the first clear definition of Computational Intelligence was introduced by Bezdek in 1994: a system is called computationally intelligent if it deals with low-level data such as numerical data, has a pattern-recognition component and does not use knowledge in the AI sense, and additionally when it begins to exhibit computational adaptively, fault tolerance, speed approaching human-like turnaround and error rates that approximate human performance. Bezdek and Marks (1993) clearly differentiated CI from AI, by arguing that the first one is based on soft computing methods, whereas AI is based on hard computing ones. Difference between Computational and Artificial Intelligence Although Artificial Intelligence and Computational Intelligence seek a similar long-term goal: reach general intelligence, which is the intelligence of a machine that could perform any intellectual task that a human being can; there's a clear difference between them. According to Bezdek (1994), Computational Intelligence is a subset of Artificial Intelligence. There are two types of machine intelligence: the artificial one based on hard computing techniques and the computational one based on soft computing methods, which enable adaptation to many situations. Hard computing techniques work following binary logic based on only two values (the Booleans true or false, 0 or 1) on which modern computers are based. One problem with this logic is that our natural language cannot always be translated easily into absolute terms of 0 and 1. Soft computing techniques, based on fuzzy logic can be useful here. Much closer to the way the human brain works by aggregating data to partial truths (Crisp/fuzzy systems), this logic is one of the main exclusive aspects of CI. Within the same principles of fuzzy and binary logics follow crispy and fuzzy systems. Crisp logic is a part of artificial intelligence principles and consists of either including an element in a set, or not, whereas fuzzy systems (CI) enable elements to be partially in a set. Following this logic, each element can be given a degree of membership (from 0 to 1) and not exclusively one of these 2 values. The five main principles of CI and its applications The main applications of Computational Intelligence include computer science, engineering, data analysis and bio-medicine. Fuzzy logic As explained before, fuzzy logic, one of CI's main principles, consists in measurements and process modelling made for real