Real-Coded Genetic Algorithms and Interval-Schemata

Eshelman, Larry J.; Schaffer, J. David

doi:10.1016/b978-0-08-094832-4.50018-0

Cited by 1,177 publications

(671 citation statements)

References 5 publications

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“…A relatively high mutation rate (0.1) is used to enable an effective exploration of the solution space. The crossover operator is BLX-α [56], that is specifically developed for a real-coded GA. BLX-α consists in, given two individuals…”

Section: Methodsmentioning

confidence: 99%

Approximate k-NN delta test minimization method using genetic algorithms: Application to time series

2010

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In many real world problems, the existence of irrelevant input variables (features) hinders the predictive quality of the models used to estimate the output variables. In particular, time series prediction often involves building large regressors of artificial variables that can contain irrelevant or misleading information. Many techniques have arisen to confront the problem of accurate variable selection, including both local and global search strategies. This paper presents a method based on genetic algorithms that intends to find a global optimum set of input variables that minimize the Delta Test criterion. The execution speed has been enhanced by substituting the exact nearest neighbor computation by its approximate version. The problems of scaling and projection of variables have been addressed. The developed method works in conjunction with MATLAB's Genetic Algorithm and Direct Search Toolbox. The goodness of the proposed methodology has been evaluated on several popular time series examples, and also generalized to other non-time-series datasets.

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Section: Methodsmentioning

confidence: 99%

Approximate k-NN delta test minimization method using genetic algorithms: Application to time series

2010

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“…The crossover operation for comparison is the UNDXBXover, which consists of two published crossover operations: Unimodal normal distribution crossover (UNDX) [12,22] and Blend crossover (BLX-α) [7]. The details of these two crossovers are shown in Appendix B and Appendix C respectively.…”

Section: B Experimental Setupmentioning

confidence: 99%

An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations

Ling

Leung

2006

Soft Comput

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This paper presents a real-coded genetic algorithm (RCGA) with new genetic operations (crossover and mutation). They are called the average-bound crossover (ABX) and wavelet mutation (WM). By introducing the proposed genetic operations, both the solution quality and stability are better than the RCGA with conventional genetic operations. A suite of benchmark test functions are used to evaluate the performance of the proposed algorithm. Application examples on economic load dispatch and tuning an associative-memory neural network are used to show the performance of the proposed RCGA.

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“…Then the parent variables of the objective function goes through a probabilistic check and participate in crossover using crossover operands [37][38][39][40]. This results in producing better children solutions.…”

Section: A Objective Functionmentioning

confidence: 99%

Maximum Solar Power Generation through Optimization of Tilt Solar Angles of Solar Panels by Heuristic Technique

T¹,

Pathak²

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:The optimal power generation from a solar power system for a detailed loading and irradiation scenario prevailing at a specific location in India has been determined. Three different types of loads reflecting economic and residential activity at the location has been considered. Daily global and diffuse solar irradiation data corresponding to two different seasons of the year have been used in the radiation model for the solar panel. The optimal operation is being determined through optimal choice of the tilt angle for the solar panel in order to collect the most concentrated solar irradiation. The mathematical technique used is a modified version of the conventional genetic algorithm (GA) termed Tuned Genetic Algorithm (TGA). TGA is robust against the above discussed variations by virtue of metaheuristic nature and is more reliable in avoiding a local minimum of the objective function than GA. The TGAevaluated total solar power generations for each loading system in India utilizing different values of tilt angles of solar panel have been compared. The solar power generation is found to exceed demand by the largest margins for all three loads in summer season during optimal power operation. For most economic operation of the solar power system during the summer season, a 60 degree angle of tilt was found to be optimal for residential loads, whereas 50 degree tilt angle was optimal for the other loads studied.

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Real-Coded Genetic Algorithms and Interval-Schemata

Cited by 1,177 publications

References 5 publications

Approximate k-NN delta test minimization method using genetic algorithms: Application to time series

Approximate k-NN delta test minimization method using genetic algorithms: Application to time series

An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations

Maximum Solar Power Generation through Optimization of Tilt Solar Angles of Solar Panels by Heuristic Technique

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