Overview
The Interdisciplinary Group in Production Engineering and Computational Intelligence was created on May 22, 2024. The group is registered in the CNPq Research Groups Directory, as shown in the following link:
dgp.cnpq.br/dgp/espelhogrupo/2955724801408791
The research group has a multidisciplinary scope, comprising various fields of knowledge such as Engineering, Computer Science, Applied Mathematics, and Economics, among others. The studies carried out by the group are related to the development of approximate methods to solve complex problems arising from real-world applications. The group aims to operate transversally across the different areas of Production Engineering, as classified by the Brazilian Association of Production Engineering – ABEPRO [1]. According to ABEPRO’s classification, Computational Intelligence (CI) is a subarea of Operations Research. CI comprises flexible computational methods to provide satisfactory solutions within an acceptable time frame for practical problems.
Artificial Intelligence (AI) and Computational Intelligence (CI) are terms often used interchangeably; however, there is a distinction between the two fields. AI is a broader area, with techniques that aim to simulate human intelligence, encompassing learning, reasoning, and language. CI can be defined as a subfield of AI whose techniques are designed to solve complex problems, often using imprecise or unstructured data. CI techniques are inspired by biological systems, physical processes, or human cognition.
Another distinction between these two fields lies in the way problems are approached. AI typically relies on hard computing, working with analytical models and exact data, whereas CI uses soft computing methods, which apply approximations and may consider randomness and uncertainty.
According to the Brazilian Society of Computational Intelligence (SBIC) [2]:
“Computational Intelligence (CI) refers to a set of bio-inspired computational methods capable of tackling complex real-world problems. CI differs from classical Artificial Intelligence (AI) based on nature-inspired models such as Artificial Neural Networks, Genetic Algorithms, or Swarm Intelligence. In contrast, AI typically employs models based on various forms of human reasoning. Computational Intelligence methods aim to perform tasks that require reasoning, learning, decision-making, and optimization. CI is also known as Bio-Inspired Computing, Natural Computing, and Soft Computing”.
According to Konar [3], the modern definition of CI is strongly influenced by biologically inspired models of machine intelligence. Engelbrecht [4] identifies the main paradigms of CI as artificial neural networks, evolutionary algorithms, and fuzzy logic. However, we will adopt a broader classification of methods and techniques that can solve Production Engineering problems within acceptable computational time frames [5].
References
[1] https://portal.abepro.org.br/profissao/
[2] https://sbic.org.br/inteligencia-computacional/
[3] KONAR, Amit. Computational Intelligence: Principles, techniques and applications. Springer Science & Business Media, 2006.
[4] ENGELBRECHT, Andries P. Computational intelligence: an introduction. John Wiley & Sons, 2007.
[5] LAHA, Dipak; MANDAL, Purnendu (Ed.). Handbook of computational intelligence in manufacturing and production management. IGI Global, 2007.
