Fuzzy Systems in Bio-inspired Computing: State-of-the-Art Literature Review

Bio-inspired computing whose examples can be found in artificial intelligence is a research method for solving problems using computer models based on the principles of biology and nature. Applications of bio-inspired computing involve genetic algorithms, neural networks, artificial immune systems, sensory networks, and others. These applications sometimes need to work under incomplete and vague data and the fuzzy set theory can help model the problem under these conditions. Literature has already employed the fuzzy set theory in bio-inspired computing such as fuzzy genetic algorithms, fuzzy artificial immune systems, and fuzzy ant colony systems. Although there are many fuzzy-enhanced bio-inspired computing approaches, a systematic review that classifies these approaches is missing in the literature. Understanding how and where to use relevant fuzzy bio-inspired computing is crucial both for academicians and practitioners. Thus, this paper presents a state-of-the-art literature review for fuzzy bio-inspired computing; tabular and graphical illustrations show the numerical results of the literature review indicating that there is a large acceleration on this research area in recent years.