Dynamic fuzzy control of genetic algorithm parameter coding

Streifel, R.J.; Marks, Robert J.; Reed, Richard A.; Choi, Jong Seob; Healy, M. J. R.

doi:10.1109/3477.764878

Cited by 20 publications

(16 citation statements)

References 8 publications

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“…Despite the recent activity and the associated progress in FAGA research [33,52,55], there remain many directions in which they may be improved or extended. In particular, we consider that the most promising future research areas are the following:…”

Section: Future Directionsmentioning

confidence: 98%

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Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions

Herrera

Lozano

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adaptive genetic algorithms, that dynamically adjust selected control parameters or genetic operators during the evolution have been built. Their objective is to offer the most appropriate exploration and exploitation behaviour to avoid the premature convergence problem and improve the final results. One of the adaptive approaches are the adaptive parameter setting techniques based on the use of fuzzy logic controllers, the fuzzy adaptive genetic algorithms (FAGAs). In this paper, we analyse the FAGAs in depth. First, we describe the steps for their design and present an instance, which is studied from an empirical point of view. Then, we propose a taxonomy for FAGAs, attending on the combination of two aspects: the level where the adaptation takes place and the way the Rule-Bases are obtained. Furthermore, FAGAs belonging to different groups of the taxonomy are reviewed. Finally, we identify some open issues, and summarise a few new promising research directions on the topic. From the results provided by the approaches presented in the literature and the experimental results achieved in this paper, an important conclusion is obtained: the use of fuzzy logic controllers to adapt genetic algorithm parameters may really improve the genetic algorithm performance.Keywords Adaptive genetic algorithms, Fuzzy logic controllers IntroductionThe behaviour of the genetic algorithms (GAs) [25] is strongly determined by the balance between exploration (to investigate new and unknown areas in a search space) and exploitation (to make use of knowledge acquired by exploration to reach better positions on the search space). The GA control parameter settings, such as mutation probability, crossover probability, and population size, are key factors in the determination of the exploitation versus exploration tradeoff. It has long been acknowledged that they have a significant impact on GA performance [28]. If poor settings are used, the exploration/exploitation balance may not be reached in a profitable way; the GA performance shall be severely affected due to the possibility of premature convergence.Finding robust control parameter settings is not a trivial task, since their interaction with GA performance is a complex relationship and the optimal ones are problemdependent [4]. Furthermore, different control parameter values may be necessary during the course of a run to induce an optimal exploration/exploitation balance. For these reasons, adaptive GAs (AGAs) have been built that dynamically adjust selected control parameters or genetic operators during the course of evolving a problem solution. Their objective is to offer the most appropriate exploration and exploitation behaviour [1,19,22,31,47,53].Fuzzy logic controllers (FLCs) [17] provide a tool which can convert the linguistic control strategy based on expert knowledge into an automatic control strategy. They are particularly suited to model the relationshi...

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Section: Future Directionsmentioning

confidence: 98%

“…In [55], an algorithm for adaptively controlling GA parameter coding using fuzzy rules is presented, which was called fuzzy GAP. It uses an intermediate mapping between the genetic strings and the search space parameters.…”

Section: C5 Dynamic Fuzzy Control Of Ga Parameter Codingmentioning

confidence: 99%

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions

Herrera

Lozano

2003

Soft Computing - A Fusion of Foundations, Methodologies and App

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“…Finally, in [174], an algorithm for adaptively controlling GA parameter coding using fuzzy rules is presented, which was called fuzzy GAP. This uses an intermediate mapping between the genetic strings and the search space parameters.…”

Section: Fuzzy Representationsmentioning

confidence: 99%

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Herrera

Lozano

2009

Intelligent Systems Reference Library

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Abstract. There are two possible ways for integrating fuzzy logic and evolutionary algorithms. The first one involves the application of evolutionary algorithms for solving optimization and search problems related with fuzzy systems, obtaining genetic fuzzy systems. The second one concerns the use of fuzzy tools and fuzzy logic-based techniques for modelling different evolutionary algorithm components and adapting evolutionary algorithm control parameters, with the goal of improving performance. The evolutionary algorithms resulting from this integration are called fuzzy evolutionary algorithms. In this chapter, we shortly introduce genetic fuzzy systems and fuzzy evolutionary algorithms, giving a short state of the art, and sketch our vision of some hot current trends and prospects. In essence, we paint a complete picture of these two lines of research with the aim of showing the benefits derived from the synergy between evolutionary algorithms and fuzzy logic.

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“…Adaptation can be introduced into coding. Some examples of adaptive coding are the delta coding [12], the dynamic parameter encoding (DPE) [49], and the fuzzy coding [50,51].…”

Section: Exploitation Versus Explorationmentioning

confidence: 99%

“…Fuzzy parameter-coding changes provides a more uniform performance in the GA search, and there is an intermediate mapping between the genetic strings and the search space parameters. Examples of fuzzy-encoding techniques are the fuzzy GA parameter coding [50] and the fuzzy coding [51]. The fuzzy coding [51] provides the value of a parameter on the basis of the optimum number of selected fuzzy sets and their effectiveness in terms of the degree of membership.…”

Section: Evolutionary Algorithms Based On Fuzzy Logicmentioning

confidence: 99%

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Zhang¹,

Wu²,

et al. 2011

Applied Computational Intelligence and Soft Computing

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Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neoDarwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

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Dynamic fuzzy control of genetic algorithm parameter coding

Cited by 20 publications

References 8 publications

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions

Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions

Fuzzy Evolutionary Algorithms and Genetic Fuzzy Systems: A Positive Collaboration between Evolutionary Algorithms and Fuzzy Systems

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

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