Evolutionary Fuzzy System
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An Evolutionary Fuzzy System is a hybrid intelligent system that integrates a fuzzy systems with evolutionary algorithm.
- AKA: Evolutionary Fuzzy Control System, Evolving Fuzzy System, EFS.
- Example(s):
- See: Fuzzy Logic, Fuzzy Markup Language, Neuro-Fuzzy, Fuzzy Set Theory, Automated Learning, Genetic Algorithm, Data Mining, Knowledge-Based System, Artificial Intelligence.
References
2016
- (Ferranti et al., 2016) ⇒ Ferranti, A., Marcelloni, F., & Segatori, A. (2016, July). A Multi-objective evolutionary fuzzy system for big data. In Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on (pp. 1562-1569). DOI:10.1109/FUZZ-IEEE.2016.7737876.
- ABSTRACT: One of the most appealing features of fuzzy rule-based classifiers is the capability of explaining how the conclusions are inferred. This feature is hard to preserve when fuzzy rules are extracted from a very large amount of data. In this paper, we propose a distributed version of PAES-RCS, a multiobjective evolutionary approach to learn concurrently the rule and data bases of fuzzy rule-based classifiers by maximizing accuracy and minimizing complexity. PAES-RCS has proven to be very efficient in obtaining satisfactory approximations of the Pareto front exploiting a limited number of iterations. We implemented the distributed version of PAES-RCS by using Apache Spark as data processing framework. We discuss the effectiveness of our approach in terms of classification rate and scalability by performing a number of experiments on three real-world big datasets. Further, we compare our approach with other well-known state-of-art algorithms in terms of both accuracy and complexity, and evaluate the achievable speedup on a small computer cluster. We show that the distributed version can efficiently extract compact rule bases with high accuracy and allows handling big datasets even with modest hardware support.
2015
- (Fernandez et al., 2015) ⇒ Fernandez, A., Lopez, V., del Jesus, M. J., & Herrera, F. (2015). Revisiting evolutionary fuzzy systems: Taxonomy, applications, new trends and challenges. Knowledge-Based Systems, 80, 109-121.
- ABSTRACT: Evolutionary Fuzzy Systems are a successful hybridization between fuzzy systems and Evolutionary Algorithms. They integrate both the management of imprecision/uncertainty and inherent interpretability of Fuzzy Rule Based Systems, with the learning and adaptation capabilities of evolutionary optimization. Over the years, many different approaches in Evolutionary Fuzzy Systems have been developed for improving the behavior of fuzzy systems, either acting on the Fuzzy Rule Base Systems’ elements, or by defining new approaches for the evolutionary components. All these efforts have enabled Evolutionary Fuzzy Systems to be successfully applied in several areas of Data Mining and engineering. In accordance with the former, a wide number of applications have been also taken advantage of these types of systems. However, with the new advances in computation, novel problems and challenges are raised every day. All these issues motivate researchers to make an effort in releasing new ways of addressing them with Evolutionary Fuzzy Systems. In this paper, we will review the progression of Evolutionary Fuzzy Systems by analyzing their taxonomy and components. We will also stress those problems and applications already tackled by this type of approach. We will present a discussion on the most recent and difficult Data Mining tasks to be addressed, and which are the latest trends in the development of Evolutionary Fuzzy Systems.
2011
- (Kavka, 2011) ⇒ Carlos Kavka. (2011). “Evolutionary Fuzzy System." In: (Sammut & Webb, 2011) p.357
- QUOTE: Definition - An evolutionary fuzzy system is a hybrid automatic learning approximation that integrates fuzzy systems with evolutionary algorithms, with the objective of combining the optimization and learning abilities of evolutionary algorithms together with the capabilities of fuzzy systems to deal with approximate knowledge. Evolutionary fuzzy systems allow the optimization of the knowledge provided by the expert in terms of linguistic variables and fuzzy rules, the generation of some of the components of fuzzy systems based on the partial information provided by the expert, and in some cases even the generation of fuzzy systems without expert information. Since many evolutionary fuzzy systems are based on the use of genetic algorithms, they are also known as genetic fuzzy systems. However, many models presented in the scientific literature also use genetic programming, evolutionary programming, or evolution strategies, making the term evolutionary fuzzy systems more adequate. Highly related is the concept of evolutionary neuro-fuzzy systems, where the main difference is that the representation is based on neural networks. Recently, the related concept of evolving fuzzy systems has been introduced, where the main objective is to apply evolutionary techniques to the design of fuzzy systems that are adequate to the control of nonstationary processes, mainly on real-time applications.
2008
- (Angelov,2008) ⇒ Plamen Angelov (2008), Scholarpedia, 3(2):6274. "Evolving fuzzy systems" DOI:10.4249/scholarpedia.6274
- QUOTE: Evolving fuzzy systems (EFS) can be defined as self-developing, self-learning fuzzy rule-based or neuro-fuzzy systems that have both their parameters but also (more importantly) their structure self-adapting on-line.
They are usually associated with streaming data and on-line (often real-time) modes of operation. In a narrower sense they can be seen as adaptive fuzzy systems. The difference is that evolving fuzzy systems assume on-line adaptation of system structure in addition to the parameter adaptation which is usually associated with the term adaptive. They also allow for adaptation of the learning mechanism. Therefore, evolving assumes a higher level of adaptation.
In this definition the English word evolving is used with its core meaning as described in the Oxford dictionary (Hornby, 1974; p.294), namely unfolding; developing; being developed, naturally and gradually.
Often evolving is used in relation to so called evolutionary and genetic algorithms. The meaning of the term evolutionary is defined in the Oxford dictionary as development of more complicated forms of life (plants, animals) from earlier and simpler forms. EFS consider a gradual development of the underlying (fuzzy or neuro-fuzzy) system structure and do not deal with such phenomena specific for the evolutionary and genetic algorithms as chromosomes crossover, mutation, selection and reproduction, parents and off-springs.
- QUOTE: Evolving fuzzy systems (EFS) can be defined as self-developing, self-learning fuzzy rule-based or neuro-fuzzy systems that have both their parameters but also (more importantly) their structure self-adapting on-line.
1999
- (Shi et al., 1999) ⇒ Shi, Y., Eberhart, R., & Chen, Y. (1999). Implementation of evolutionary fuzzy systems. IEEE Transactions on fuzzy systems, 7(2), 109-119.
- ABSTRACT: In this paper, evolutionary fuzzy systems are discussed in which the membership function shapes and types and the fuzzy rule set including the number of rules inside it are evolved using a genetic (evolutionary) algorithm. In addition, the genetic parameters (operators) of the evolutionary algorithm are adapted via a fuzzy system. Benefits of the methodology are illustrated in the process of classifying the iris data set. Possible extensions of the methods are summarized.