Genetic programming and bacterial algorithm for neural networks and fuzzy systems design (original) (raw)

Botzheim, J. et al. Paper: Genetic and Bacterial Programming for B-Spline Neural Networks Design

2006

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuris-tics have been found to be highly dependent on the ini-tial conditions employed. Increasing interest in bio-logically inspired learning algorithms for control tech-niques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phe-nomenon. This technique produces an efficient topol-ogy search, obtaining additionally more consistent so-lutions.

Genetic and bacterial programming for B-spline neural networks design

2007

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Hierarchical fuzzy system modeling by Genetic and Bacterial Programming approaches

International Conference on Fuzzy Systems, 2010

In this paper a method is proposed for constructing hierarchical fuzzy rule bases in order to model black box systems defined by input-output pairs, i.e. to solve supervised machine learning problems. The resultant hierarchical rule base is the knowledge base, which is constructed by using structure constructing evolutionary techniques, namely, Genetic and Bacterial Programming Algorithms. Applying hierarchical fuzzy rule bases is a way of reducing the complexity of the knowledge base, whereas evolutionary methods ensure a relatively efficient learning process. This is the reason of the investigation of this combination.

Design of B-spline neural networks using a bacterial programming approach

2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), 2004

The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.

A Comparative Study on Applying Bio-Inspired Optimization Techniques for Designing an Effective Fuzzy Logic Controller

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2020

A fuzzy logic – based controller has been considered to be a feasible and effective control strategy for a numerous number of complex control problems. To design such an effective fuzzy logic controller, it is necessary to determine proper scaling factors which significantly affect the control quality of the system. This study concentrates on applying well-known bio-inspired optimization methods, i.e. PSO, GA and DE, to deal with this determination. A typical PD-type fuzzy logic architecture is chosen to be a traditionally intelligent controller. Then, the bio-inspired optimization methods will be applied for such a fuzzy logic controller to optimally determine its three scaling factors. The simulation results provided for the load – frequency control issue of a two-area interconnected hydropower system verifies the applicability of the proposed control strategy. Comparative simulations are also to decide which is the best choice of the bio-inspired optimization methods in the determination of significant scaling factors regarding the PD-type fuzzy logic controller.

Evolutionary versus inductive construction of neurofuzzy systems for bioprocess modelling

Second International Conference on Genetic Algorithms in Engineering Systems, 1997

The control and optimization of biotechnological processes is a complex task of industrial relevance, due to the growing importance attached to biotechnology. Therefore, there is an increasing use of intelligent data analysis methods for the development and optimization of bioprocess modelling and control. Since a clear understanding of the underlying physics does not exist, nonlinear learning systems, which can accurately model exemplar data sets and explain their behaviour to the designer, are an attractive approach. This paper investigates applying neurofuzzy construction algorithms to this problem and in particular compares a Genetic Programming structuring approach with a more conventional forwards inductive learning-type algorithm. It is shown that for simple problems, the inductive learning technique generally outperforms the Genetic Programming, although for large complex problems, the latter may prove beneficial.

Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases

2001

Rendón, The fuzzy classifier system: motivations and first results, Proc. First Intl. Conf. on Parallel Problem Solving from Nature-PPSN I, Springer, Berlin, 1991, pp. 330-334 (scatter Mamdani fuzzy rules for control/modeling problems) M. Valenzuela-Rendón, Reinforcement learning in the fuzzy classifier system, Expert Systems with Applications 14 (1998) 237-247 (scatter Mamdani fuzzy rules for control/modeling problems) J.R. Velasco, Genetic-based on-line learning for fuzzy process control, IJIS 13 (10-11) (1998) 891-903 (scatter Mamdani fuzzy rules for control problems)

A genetic-based neuro-fuzzy approach for modeling and control of dynamical systems

IEEE Transactions on Neural Networks, 1998

Linguistic modeling of complex irregular systems constitutes the heart of many control and decision making systems, and fuzzy logic represents one of the most effective algorithms to build such linguistic models. In this paper, a linguistic (qualitative) modeling approach is proposed. The approach combines the merits of the fuzzy logic theory, neural networks, and genetic algorithms (GA’s). The proposed model is presented in a fuzzy-neural network (FNN) form which can handle both quantitative (numerical) and qualitative (linguistic) knowledge. The learning algorithm of an FNN is composed of three phases. The first phase is used to find the initial membership functions of the fuzzy model. In the second phase, a new algorithm is developed and used to extract the linguistic-fuzzy rules. In the third phase, a multiresolutional dynamic genetic algorithm (MRD-GA) is proposed and used for optimized tuning of membership functions of the proposed model. Two well-known benchmarks are used to evaluate the performance of the proposed modeling approach, and compare it with other modeling approaches.