Guest Editorial: Special issue on advances in computational intelligence paradigms and applications

Abstract

Computational Intelligence (CI) is an emerging field covering a highly interdisciplinary methodological framework that is useful for supporting the design, analysis, and deployment of intelligent systems. According to Bezdek (1994), “. . . a system is computationally intelligent when it: deals only with numerical (low-level) data, has a pattern recognition component, and does not use knowledge in the AI sense; and additionally when it (begins to) exhibit (i) computational adaptivity; (ii) computational fault tolerance; (iii) speed approaching human-like turnaround, and (iv) error rates that approximate human performance . . .”. Indeed, CI involves innovative models with a high level of machine learning quotient that combine elements of learning, adaptation, and evaluation. Examples of CI paradigms include fuzzy computing, neural computing, evolutionary computing, probabilistic computing, rough set theory, knowledge-based systems, adaptive learning algorithms, and hybrids of these paradigms. These techniques can be applied to a wide range of problems including optimization, decision making, information processing, pattern recognition, and intelligent data analysis. In this special issue, a number of papers that address theoretical advances as well as practical applications of various CI techniques are presented. The first two papers cover investigation into fuzzy measures and intelligent data analysis. The next two