COMPUTATIONAL INTELLIGENCE: FOUNDATIONS, PERSPECTIVES, AND RECENT TRENDS
The field of computational intelligence has evolved with the objective of developing machines that can think like humans. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, understanding, learning, and so on. Computational intelligence includes neural networks, fuzzy inference systems, global optimization algorithms, probabilistic computing, swarm intelligence, and so on. This chapter introduces the fundamental aspects of the key components of modern computational intelligence. It presents a comprehensive overview of various tools of computational intelligence (e.g., fuzzy logic, neural network, genetic algorithm, belief network, chaos theory, computational learning theory, and artificial life). The synergistic behavior of the above tools on many occasions far exceeds their individual performance. A discussion on the synergistic behavior of neuro-fuzzy, neuro-genetic algorithms (GA), neuro-belief, and fuzzy-belief network models is also included in the chapter.
1.1 WHAT IS COMPUTATIONAL INTELLIGENCE?
Machine Intelligence refers back to 1936, when Turing proposed the idea of a universal mathematics machine [1,2], a theoretical concept in the mathematical theory of computability. Turing and Post independently proved that determining the decidability of mathematical propositions ...