Modal mutations in evolutionary algorithms

Voigt, Hans-Michael; Anheyer, T.

doi:10.1109/icec.1994.350036

Cited by 18 publications

(14 citation statements)

References 9 publications

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“…Polygeny is the effect when a single phenotypic characteristic may be determined by the simultaneous interaction of many genes [58]. An attempt to deal with more complex genotype/phenotype relations in EAs was presented in [191,194]. A fuzzy representation is proposed for the case of tackling optimization problems of parameters with variables on continuous domains.…”

Section: Fuzzy Representationsmentioning

confidence: 99%

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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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Section: Fuzzy Representationsmentioning

confidence: 99%

“…In [192,193,195], crossover and mutation operators were presented, which are based on the use of triangular probability distributions. These operators, called soft modal crossover and mutation, are a generalization of the discrete crossover operator and the BGA mutation, respectively, proposed for the Breeder GA [141].…”

Section: Fuzzy Genetic Operatorsmentioning

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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“…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]. More refined methods include the automatic tuning of a fuzzy neural network by a genetic algorithm ( [64]) and fuzzy classification methods based on neural networks and genetic algorithms ( [144]).…”

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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Section: Mutation Operatorsmentioning

confidence: 99%

“…The perturbation created in a chromosome is within a 10% range of what is permissible for a particular gene. The design concepts of discrete and continuous modal mutations [31] are similar to that of the BGA. Non-uniform mutation (NUM) [32] possesses a finetuning capability whereby its action depends on the number of the population in order to reach equilibrium between exploration and exploitation.…”

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confidence: 99%

Crossover and Mutation Operators of Genetic Algorithms

Lim¹,

Sultan²,

Sulaiman³

et al. 2017

IJMLC

103

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Abstract-Genetic algorithms (GA) are stimulated by population genetics and evolution at the population level where crossover and mutation comes from random variables. The problems of slow and premature convergence to suboptimal solution remain an existing struggle that GA is facing. Due to lower diversity in a population, it becomes challenging to locally exploit the solutions. In order to resolve these issues, the focus is now on reaching equilibrium between the explorative and exploitative features of GA. Therefore, the search process can be prompted to produce suitable GA solutions. This paper begins with an introduction, Section 2 describes the GA exploration and exploitation strategies to locate the optimum solutions. Section 3 and 4 present the lists of some prevalent mutation and crossover operators. This paper concludes that the key issue in developing a GA is to deliver a balance between explorative and exploitative features that complies with the combination of operators in order to produce exceptional performance as a GA as a whole.Index Terms-Crossover operator, mutation operator, exploitation, exploration. I. INTRODUCTIONThe main search operator in Genetic algorithms (GA) is the crossover operator which equally as significant as mutation, selection and coding in GA. The crossover operator functions primarily in the survey of information that is accessible through the search space, which inadvertently improves the behavior of the GA. On another note, mutation is a secondary operator. It functions to alter the genes of the offspring. A mutator will diversify the existing population and this inadvertently allows GAs to exploit promising areas of the search space thus avoiding local solutions [1]. Some of the mutation operators are designed to explicitly overcome certain types of issues over others [2]. The performance among all the comparative of GA operators are easily validated and compared through unbiased test problems from the literature, which are diverse in properties in terms of complexity and modality. This study substantially contributes in reviewing some prevalent mutation and crossover operators. The operators maintain a good balance between explorative and exploitative strategies while manufacturing the optimum GA solutions. II. ACHIEVING EXPLORATION AND EXPLOITATION IN GENETIC ALGORITHMA crossover or mutation can function as an exploration or exploitation operator [3], [4]. Although optimization algorithms with higher degree of exploitation may have Manuscript received August 30, 2016; revised December 8, 2016. Siew Mooi Lim is with University Malaysia of Computer Science and Engineering, Malaysia (e-mail: limsm66@gmail.com).higher convergence speed, the challenge lies in locating the optimal solution and chances are it may not get past a local optimum. On the other hand, algorithms that favor exploration over exploitation might consume more time in locating the global optimum, that is, coincidentally, due to its less sophisticated candidate solutions. A comprehensive survey in exploratio...

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Modal mutations in evolutionary algorithms

Cited by 18 publications

References 9 publications

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

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

An Overview of Fuzzy Control Theory

Crossover and Mutation Operators of Genetic Algorithms

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