Optimization of fuzzy rule sets using a bacterial evolutionary algorithm

In this paper we present a novel approach where we rst create a large set of (possibly) redundant rules using inductive rule learning and where we use a bacterial evolutionary algorithm to identify the best subset of rules in a subsequent step. This enables us to nd an optimal rule set with respect to a freely de nable global goal function, which gives us the possibility to integrate interpretability related quality criteria explicitly in the goal function and to consider the interplay of the overlapping fuzzy rulesPeer Reviewe

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use

A Dispersion Operator for Geometric Semantic Genetic Programming

Recent advances in geometric semantic genetic programming (GSGP) have shown that the results obtained by these methods can outperform those obtained by classical genetic programming algorithms, in particular in the context of symbolic regression. However, there are still many open issues on how to improve their search mechanism. One of these issues is how to get around the fact that the GSGP crossover operator cannot generate solutions that are placed outside the convex hull formed by the individuals of the current population. Although the mutation operator alleviates this problem, we cannot guarantee it will find promising regions of the search space within feasible computational time. In this direction, this paper proposes a new geometric dispersion operator that uses multiplicative factors to move individuals to less dense areas of the search space around the target solution before applying semantic genetic operators. Experiments in sixteen datasets show that the results obtained by the proposed operator are statistically significantly better than those produced by GSGP and that the operator does indeed spread the solutions around the target solution

Optimization of fuzzy rule sets using a bacterial evolutionary algorithm

In this paper we present a novel approach where we rst create a large set of (possibly) redundant rules using inductive rule learning and where we use a bacterial evolutionary algorithm to identify the best subset of rules in a subsequent step. This enables us to nd an optimal rule set with respect to a freely de nable global goal function, which gives us the possibility to integrate interpretability related quality criteria explicitly in the goal function and to consider the interplay of the overlapping fuzzy rulesPeer Reviewe

Fuzzy model identification by evolutionary, gradient based and memtic algorithms

One of the crucial problems of fuzzy rule modeling is how to find an optimal or at least a quasi-optimal rule base fro a certain system. In most applications there is no human expert available, or, the result of a human expert's decision is too much subjective and is not reproducible, thus some automatic method to determine the fuzzy rule base must be deployed

Extension of the Levenberg-Marquardt algorithm for the extraction of trapezoidal and general piecewise linear fuzzy rules

This paper discusses how training algorithms for determining membership functions in fuzzy rule based systems can be applied. There are several training algorithms, wbicb have been developed initially for neural networks mnd can be adapted to fumy systems. In this paper the Levenberg-Marquardt algorithm is introduced, allowing the determination of an optimal rukbase and converging faster tban some more classic methods (e.g. the standard Back Propagation algorithm). The class of membership funetions investigated is the trapezoidal one as it is general enough and widely used. The method can be easily extended to arbitrary piecewise linear function as well