Recent Trends in Computational Intelligence: Paradigms and Applications

Abstract

To be intelligent is to be able to think and reason, to be able to grasp, understand, and 
benefit from one's own experience. An umbrella term for the three basic technologies of artificial 
neural networks, fuzzy systems and evolutionary computing is computational intelligence (CI). These 
clever algorithms are a component of the area of artificial intelligence, along with logic, deductive 
reasoning, expert systems, case-based reasoning, and symbolic machine learning systems (AI). For 
example, computer science, philosophy, sociology, and biology are all examples of study fields that 
may be combined in this way. It is the study of adaptive processes that permit or assist intelligent 
behaviour in complex and changing contexts (CI). These are AI paradigms that are able to learn or 
adapt to new contexts, to generalise, abstract, discover, and associate with one other. The CI 
paradigms of artificial neural networks, evolutionary computation, swarm intelligence, artificial 
immune systems, and fuzzy systems are among the CI paradigms to be explored further. Since no 
one paradigm is better to the others in all scenarios, the current tendency is to construct hybrids of 
various Computational Intelligence (CI) approaches, which have proven useful in solving real-world 
issues. Our goal is to take use of each component's strengths while also eliminating its flaws in the 
hybrid Computational Intelligence (CI) system. Computational Intelligence and its concepts and 
applications are discussed in this article.