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Journal ArticleDOI

Empirical Analysis and Random Respectful Recombination of Crossover and Mutation in Genetic Algorithms

12 Jan 2010-International Journal of Computer Applications (Foundation of Computer Science FCS)-Iss: 1, pp 25-30
TL;DR: This paper studies the problem of how changes in the four GA parameters (population size, number of generations, crossover & mutation probabilities) have an effect on GA’s performance from a practical stand point and tests the robustness of GA to control parameters.
Abstract: Genetic algorithms (GAs) are multi-dimensional, blind & heuristic search methods which involves complex interactions among parameters (such as population size, number of generations, various type of GA operators, operator probabilities, representation of decision variables etc.). Our belief is that GA is robust with respect to design changes. The question is whether the results obtained by GA depend upon the values given to these parameters is a matter of research interest. This paper studies the problem of how changes in the four GA parameters (population size, number of generations, crossover & mutation probabilities) have an effect on GA’s performance from a practical stand point. To examine the robustness of GA to control parameters, we have tested two groups of parameters & the interaction inside the group (a) Crossover & mutation alone (b) Crossover combined with mutation . Based on calculations and simulation results it is seen that for simple problems mutation plays an momentous role. For complex problems crossover is the key search operator. Based on our study complementary crossover & mutation probabilities is a reliable approach.
Citations
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Journal ArticleDOI
TL;DR: The results of experiments demonstrate that the optimized rule obtained using the GA can increase the profit generated significantly as compare to traditional moving average lengths trading rules taken from financial literature.
Abstract: Recent studies have shown that in the context of financial markets, technical analysis is a very useful tool for predicting trends. Moving Average rules are usually used to make “buy” or “sell” decisions on a daily basis. Due their ability to cover large search spaces with relatively low computational effort, Genetic Algorithms (GA) could be effective in optimization of technical trading systems. This paper studies the problem: how can GA be used to improve the performance of a particular trading rule by optimizing its parameters, and how changes in the design of the GA itself can affect the solution quality obtained in context of technical trading system. In our study, we have concentrated on exploiting the power of genetic algorithms to adjust technical trading rules parameters in background of financial markets. The results of experiments based on real timeseries data demonstrate that the optimized rule obtained using the GA can increase the profit generated significantly as compare to traditional moving average lengths trading rules taken from financial literature.

8 citations


Additional excerpts

  • ...Mutation operator is used to add random diversity in the solution [16]....

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Journal ArticleDOI
Abstract: Chili oil, which contains large amounts of capsaicin and capsorubin, is one of the most consumed seasonings in China. These compounds significantly affect the quality, antioxidant activity, pungency, and color of chili oil. This study aimed to investigate the effect of stewing temperature, stewing time, and amount of oil on the capsaicin and capsorubin contents of Chinese chili oil. The partial least squares (PLS) regression and genetic algorithm–artificial neural network models were established and used to predict capsaicin and capsorubin contents. The genetic algorithm was applied to optimize the parameters of the network. The developed genetic algorithm–artificial neural network, which included ten hidden neurons, predicted capsaicin and capsorubin contents with correlation coefficients of 0.995 and 0.986, respectively. The neural network exhibited more accurate prediction and practicability compared with the PLS regression model.

6 citations


Cites background from "Empirical Analysis and Random Respe..."

  • ...Empirical analysis and random recombination of crossover and mutations in GA were reported by Kapoor et al. (2010)....

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Journal Article
TL;DR: The methodology provides the novel function of adaptive parameter adjustment during each evolution generation of GA to enhance search efficiency towards optimal solutions and improve search effectiveness and algorithm robustness.
Abstract: This paper presents a new methodology for improving the efficiency and generality of Genetic Algorithms (GA). The methodology provides the novel function of adaptive parameter adjustment during each evolution generation of GA. The important characteristics of the methodology are mainly from the following two aspects: (1) superior performance members in GA are preserved and inferior performance members are deteriorated to enhance search efficiency towards optimal solutions; (2) adaptive crossover and mutation management is applied in GA based on the transformation functions to explore wider spaces so as to improve search effectiveness and algorithm robustness. The research was successfully applied for a luggage design chain to generate optimal solutions (minimized lifecycle cost). Experiments were conducted to compare the work with the prior art to demonstrate the characteristics and advantages of the research.

5 citations


Journal ArticleDOI
Abstract: Abstract The aim of this study was to investigate the effect of different pretreatment and reducing sugar content on furfural (F) and 5-hydroxymethylfurfural (HMF) contents of fermented lotus root by vinegar. The lotus root samples were fermented using vinegar for 15 days, at different solution concentrations and temperatures. The processing conditions were considered as inputs of neural network to predict the F and HMF contents of lotus root. Genetic algorithm was applied to optimize the structure and learning parameters of ANN. The developed genetic algorithm-artificial neural network (GA-ANN) which included 23 and 17 neurons in the first and second hidden layers, respectively, gives the lowest mean squared error (MSE). The correlation coefficient of ANN was compared with multiple linear regression-based models. The GA-ANN model was found to be a more accurate prediction method for the F and HMF contents of fermented lotus root than linear regression-based models. In addition, sensitivity analysis and Pearson’s correlation coefficient were also analyzed to find out the relation between input and output variables.

4 citations


Cites background from "Empirical Analysis and Random Respe..."

  • ...reported that the set of crossover and mutation parameters [29]....

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Proceedings ArticleDOI
23 May 2012-
TL;DR: This study presents the uncertainty remanufacturing demand (URD) for green suitcase chain to predict the return demand model of an flexible inventory model to improve the effectiveness of extended producer responsibility for green supply chain management.
Abstract: This study presents the uncertainty remanufacturing demand (URD) for green suitcase chain to predict the return demand model of an flexible inventory model. In this research, we proposed reverse production design by green prediction model for the division of green cost responsibility. This philosophy have become a popular topic that improved the effectiveness of extended producer responsibility for green supply chain management (GSCM). It is different from the traditional division of green cost responsibility processes that we proposed a novel measurement by the uncertainty index from green prediction model evolutions. A grey genetic algorithm (GGA) was designed by adaptive designs for URD optimization. These designs provided a novel evaluation index by varying all variables to achieve the global optimization of green cost responsibility. The new demand prediction design derived from the crossover and mutation rate of an adjusted GA search optimization. This research verified these methodologies in a practical case. The experiment is simulated by a GGA to reach an optimal solution.

2 citations


Cites background from "Empirical Analysis and Random Respe..."

  • ...The exploitation is able to be prompted by selected suitable pressure components and their parameters such as population size, crossover rate and mutation rate [10] [11] [25]....

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References
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Book
01 Sep 1988-
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required

52,793 citations


01 Jan 1989-
TL;DR: This book brings together the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields.
Abstract: From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields. Major concepts are illustrated with running examples, and major algorithms are illustrated by Pascal computer programs. No prior knowledge of GAs or genetics is assumed, and only a minimum of computer programming and mathematics background is required.

32,374 citations


"Empirical Analysis and Random Respe..." refers background in this paper

  • ...With this in mind it is suggested that GAs will work well with high crossover & low mutation probability (Goldberg, 1989)....

    [...]

  • ...See Goldberg (1989)....

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Book ChapterDOI
01 Jan 1991-
TL;DR: A number of selection schemes commonly used in modern genetic algorithms are compared on the basis of solutions to deterministic difference or differential equations, verified through computer simulations to provide convenient approximate or exact solutions and useful convergence time and growth ratio estimates.
Abstract: This paper considers a number of selection schemes commonly used in modern genetic algorithms. Specifically, proportionate reproduction, ranking selection, tournament selection, and Genitor (or “steady state”) selection are compared on the basis of solutions to deterministic difference or differential equations, which are verified through computer simulations. The analysis provides convenient approximate or exact solutions as well as useful convergence time and growth ratio estimates. The paper recommends practical application of the analyses and suggests a number of paths for more detailed analytical investigation of selection techniques.

2,410 citations


6


"Empirical Analysis and Random Respe..." refers methods in this paper

  • ...Based on previous studies following salient observations are being made: Analysis of various selection schemes used in modern GA such as Roulette wheel, rank, tournament & steady state is done by Goldberg & Deb (1991)....

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01 Jan 1989-

2,278 citations


"Empirical Analysis and Random Respe..." refers background in this paper

  • ...With this in mind it is suggested that GAs will work well with high crossover & low mutation probability (Goldberg, 1989)....

    [...]

  • ...See Goldberg (1989)....

    [...]


Journal ArticleDOI
01 Apr 1994-
TL;DR: An efficient approach for multimodal function optimization using genetic algorithms (GAs) and the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA are described.
Abstract: In this paper we describe an efficient approach for multimodal function optimization using genetic algorithms (GAs). We recommend the use of adaptive probabilities of crossover and mutation to realize the twin goals of maintaining diversity in the population and sustaining the, convergence capacity of the GA. In the adaptive genetic algorithm (AGA), the probabilities of crossover and mutation, p/sub c/ and p/sub m/, are varied depending on the fitness values of the solutions. High-fitness solutions are 'protected', while solutions with subaverage fitnesses are totally disrupted. By using adaptively varying p/sub c/ and p/sub ,/ we also provide a solution to the problem of deciding the optimal values of p/sub c/ and p/sub m/, i.e., p/sub c/ and p/sub m/ need not be specified at all. The AGA is compared with previous approaches for adapting operator probabilities in genetic algorithms. The Schema theorem is derived for the AGA, and the working of the AGA is analyzed. We compare the performance of the AGA with that of the standard GA (SGA) in optimizing several nontrivial multimodal functions with varying degrees of complexity. >

2,203 citations


"Empirical Analysis and Random Respe..." refers background in this paper

  • ...Adaptive crossover and mutation probabilities helps in locating global optimum in a multimodal landscape (Srinivas & Patnaik, 1994)....

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Performance
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No. of citations received by the Paper in previous years
YearCitations
20221
20211
20161
20141
20122
20112