Decision support system based on hierarchical co-evolutionary fuzzy approach: A case study in detecting gamma ray signals

Decision support systems are powerful technologies for complex decision making and problem solving. However, constructing an accurate and interpretable decision support system (DSS) for any domain is a challenge. In this paper, a novel hierarchical co-evolutionary fuzzy system called HiCEFS is presented that can autonomously derive a fuzzy rule-based DSS from exemplar data. Most of the important components in HiCEFS, including irregular shaped membership functions (ISMFs) and fuzzy rules, are generated using a hierarchical co-evolutionary genetic algorithm that simultaneously co-evolves these components in separate genetic populations. Owing to its generic learning capability, the HiCEFS approach can be easily applied to produce DSSs for classification and regression tasks in various domains. As a case study, HiCEFS is employed to construct a DSS for detecting gamma ray signals. Experimental results show that the system is able to successfully discern the gamma rays from background hadrons, and performs superior to other established techniques.

► We present a novel hierarchical co-evolutionary fuzzy system called HiCEFS.
► HiCEFS automatically generates membership functions and fuzzy rules from data.
► The system can produce DSSs for classification and regression tasks in many domains.
► In this case study we successfully construct a DSS for detecting gamma ray signals.
► Experiments show that HiCEFS performs better than other established techniques.