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Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

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Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS’s parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods.

This article is categorized under:

  • Technologies > Computational Intelligence
  • Technologies > Machine Learning
Generic diagram of an EFS
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Example of recombination operators applied in GPFIS model solutions
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Set of premises encoded by a multi‐gene genetic programming individual
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Diagram describing the main stages of GPFIS model inference process
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Main stages of the GPFIS model
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Example of GP‐COACH DNF fuzzy rule type
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Example of a fuzzy rule created using a GA with its counterpart GP
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Synthesizing process of a GCCL‐type EFS
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Synthesis of an IRL‐type EFS
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Global level description of the synthesizing process of a Michigan‐type EFS
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Macro level description of the synthesizing process of a Pittsburgh‐type EFS
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Global perspective of the areas in which an EA can operate over a FIS
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Example of granularity learning
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Example of rule base screening through a binary GA individual –only the rules for which the values are 1 are kept in the screened rule base
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Different ways to manipulate the shape and location of a membership function
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Components of an EFS structure that an EA can act over
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