Toward a fuzzy government of genetic populations

Arnone, S.; Dell'Orto, Mariagrazia; Tettamanzi, Andrea G. B.

doi:10.1109/tai.1994.346439

Cited by 17 publications

(6 citation statements)

References 3 publications

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“…In [3], it is claimed that GAs require human supervision during their routine use as practical tools for the following reasons: (1) detect the emergence of a solution, (2) tune algorithm parameters, and (3) monitor the evolution process in order to avoid undesirable behaviour such as premature convergence. It is advised as well that any attempt to develop artificial intelligence tools based on GAs should take these issues into account.…”

Section: C3 Fuzzy Governmentmentioning

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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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: C3 Fuzzy Governmentmentioning

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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“…In [10], it is claimed that genetic programming requires human supervision during their routine use as practical tools for the following reasons: 1) to detect the emergence of a solution, 2) to tune algorithm parameters and 3) to monitor the evolution process in order to avoid undesirable behaviour such as premature convergence. It is also advised that any attempt to develop artificial intelligence tools based on genetic programming should take these issues into account.…”

Section: Fuzzy Genetic Programmingmentioning

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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“…Other work includes the use of fuzzy connectives on crossover operators, work which is largely due to Herrera et al ( [55], [56], [54], [59]), fuzzy control processes of the genetic algorithm population ( [4], [154]), the application of fuzzy control to the constraints of a genetic algorithm ( [112]), improved optimization problems ( [118], [151], [158]) and applications in soft computing ( [133], [134]). More general results about fuzzy genetic algorithms can be found in [51], [58], [53], [80] [83], [98], [137], [152], [153], and [156].…”

Section: Fuzzy-genetic Hybrid Systemsmentioning

confidence: 99%

An Overview of Fuzzy Control Theory

Peeters

2008

Foundations of Generic Optimization

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This chapter may serve as an introductory article, and is meant to give an overview of the mathematical methods applied in fuzzy control techniques, such as fuzzification, aggregation and defuzzification. We will also discuss the advantages and disadvantages of the several techniques, with respect to the achievability of their goals, and we will give a brief overview of "hybrid techniques", techniques that involves fuzzy control as well as other artificial intelligent computing methods, such as neural networks and genetic algorithms.

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Toward a fuzzy government of genetic populations

Cited by 17 publications

References 3 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

An Overview of Fuzzy Control Theory

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