Готові списки джерел за темами / REAL CODED GENETIC ALGORITHM (RCGA)

Добірка наукової літератури з теми "REAL CODED GENETIC ALGORITHM (RCGA)"

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Опубліковано: 26 жовтня 2023

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Статті в журналах з теми "REAL CODED GENETIC ALGORITHM (RCGA)"

Beklaryan, Gayane L., Andranik S. Akopov, and Nerses K. Khachatryan. "Optimisation of System Dynamics Models Using a Real-Coded Genetic Algorithm with Fuzzy Control." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 87–103. http://dx.doi.org/10.2478/cait-2019-0017.

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Abstract This paper presents a new real-coded genetic algorithm with Fuzzy control for the Real-Coded Genetic Algorithm (F-RCGA) aggregated with System Dynamics models (SD-models). The main feature of the genetic algorithm presented herein is the application of fuzzy control to its parameters, such as the probability of a mutation, type of crossover operator, size of the parent population, etc. The control rules for the Real-Coded Genetic Algorithm (RCGA) were suggested based on the estimation of the values of the performance metrics, such as rate of convergence, processing time and remoteness from a potential extremum. Results of optimisation experiments demonstrate the greater time-efficiency of F-RCGA in comparison with other RCGAs, as well as the Monte-Carlo method. F-RCGA was validated by using well-known test instances and applied for the optimisation of characteristics of some system dynamics models.

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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation." Cybernetics and Information Technologies 21, no. 3 (September 1, 2021): 127–44. http://dx.doi.org/10.2478/cait-2021-0034.

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Abstract This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

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Mahmudy, Wayan F., Romeo M. Marian, and Lee H. S. Luong. "Real Coded Genetic Algorithms for Solving Flexible Job-Shop Scheduling Problem - Part II: Optimization." Advanced Materials Research 701 (May 2013): 364–69. http://dx.doi.org/10.4028/www.scientific.net/amr.701.364.

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This paper addresses optimization of the flexible job-shop problem (FJSP) by using real-coded genetic algorithms (RCGA) that use an array of real numbers as chromosome representation. The first part of the papers has detailed the modelling of the problems and showed how the novel chromosome representation can be decoded into solution. This second part discusses the effectiveness of each genetic operator and how to determine proper values of the RCGAs parameters. These parameters are used by the RCGA to solve several test bed problems. The experimental results show that by using only simple genetic operators and random initial population, the proposed RCGA can produce promising results comparable to those achieved by other best-known approaches in the literatures. These results demonstrate the robustness of the RCGA.

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Uemura, Kento, and Isao Ono. "AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation." Journal of Advanced Computational Intelligence and Intelligent Informatics 20, no. 3 (May 19, 2016): 429–37. http://dx.doi.org/10.20965/jaciii.2016.p0429.

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This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

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Selvakumari Jeya, I. Jasmine, and S. N. Deepa. "Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier." Computational and Mathematical Methods in Medicine 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/7493535.

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A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

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Cherif, Imen, and Farhat Fnaiech. "Nonlinear System Identification with a Real–Coded Genetic Algorithm (RCGA)." International Journal of Applied Mathematics and Computer Science 25, no. 4 (December 1, 2015): 863–75. http://dx.doi.org/10.1515/amcs-2015-0062.

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Abstract This paper is devoted to the blind identification problem of a special class of nonlinear systems, namely, Volterra models, using a real-coded genetic algorithm (RCGA). The model input is assumed to be a stationary Gaussian sequence or an independent identically distributed (i.i.d.) process. The order of the Volterra series is assumed to be known. The fitness function is defined as the difference between the calculated cumulant values and analytical equations in which the kernels and the input variances are considered. Simulation results and a comparative study for the proposed method and some existing techniques are given. They clearly show that the RCGA identification method performs better in terms of precision, time of convergence and simplicity of programming.

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Akopov, Andranik S., Levon A. Beklaryan, and Armen L. Beklaryan. "Cluster-Based Optimization of an Evacuation Process Using a Parallel Bi-Objective Real-Coded Genetic Algorithm." Cybernetics and Information Technologies 20, no. 3 (September 1, 2020): 45–63. http://dx.doi.org/10.2478/cait-2020-0027.

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AbstractThis work presents a novel approach to the design of a decision-making system for the cluster-based optimization of an evacuation process using a Parallel bi-objective Real-Coded Genetic Algorithm (P-RCGA). The algorithm is based on the dynamic interaction of distributed processes with individual characteristics that exchange the best potential decisions among themselves through a global population. Such an approach allows the HyperVolume performance metric (HV metric) as reflected in the quality of the subset of the Pareto optimal solutions to be improved. The results of P-RCGA were compared with other well-known multi-objective genetic algorithms (e.g., -MOEA, NSGA-II, SPEA2). Moreover, P-RCGA was aggregated with the developed simulation of the behavior of human agent-rescuers in emergency through the objective functions to optimize the main parameters of the evacuation process.

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Nakane, Takumi, Xuequan Lu, and Chao Zhang. "A Search History-Driven Offspring Generation Method for the Real-Coded Genetic Algorithm." Computational Intelligence and Neuroscience 2020 (September 27, 2020): 1–20. http://dx.doi.org/10.1155/2020/8835852.

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In evolutionary algorithms, genetic operators iteratively generate new offspring which constitute a potentially valuable set of search history. To boost the performance of offspring generation in the real-coded genetic algorithm (RCGA), in this paper, we propose to exploit the search history cached so far in an online style during the iteration. Specifically, survivor individuals over the past few generations are collected and stored in the archive to form the search history. We introduce a simple yet effective crossover model driven by the search history (abbreviated as SHX). In particular, the search history is clustered, and each cluster is assigned a score for SHX. In essence, the proposed SHX is a data-driven method which exploits the search history to perform offspring selection after the offspring generation. Since no additional fitness evaluations are needed, SHX is favorable for the tasks with limited budget or expensive fitness evaluations. We experimentally verify the effectiveness of SHX over 15 benchmark functions. Quantitative results show that our SHX can significantly enhance the performance of RCGA, in terms of both accuracy and convergence speed. Also, the induced additional runtime is negligible compared to the total processing time.

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Padmanabhan, S., M. Chandrasekaran, P. Asokan, and V. Srinivasa Raman. "A Performance Study of Real Coded Genetic Algorithm on Gear Design Optimization." Advanced Materials Research 622-623 (December 2012): 64–68. http://dx.doi.org/10.4028/www.scientific.net/amr.622-623.64.

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he major problem that deals with practical engineers is the mechanical design and creativeness. Mechanical design can be defined as the choice of materials and geometry, which satisfies, specified functional requirements of that design. A good design has to minimize the most significant adverse result and to maximize the most significant desirable result. An evolutionary algorithm offers efficient ways of creating and comparing a new design solution in order to complete an optimal design. In this paper a type of Genetic Algorithm, Real Coded Genetic Algorithm (RCGA) is used to optimize the design of helical gear pair and a combined objective function with maximizes the Power, Efficiency and minimizes the overall Weight, Centre distance. The performance of the proposed algorithms is validated through LINGO Software and the comparative results are analyzed.

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Lin, W., M. H. Wu, and S. Duan. "Engine Test Data Modelling by Evolutionary Radial Basis Function Networks." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 217, no. 6 (June 1, 2003): 489–97. http://dx.doi.org/10.1243/095440703766518113.

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The engine test bed is introduced briefly and the importance of modelling for the engine test is discussed. The application of combining radial basis function (RBF) networks and a real-coded genetic algorithm (RCGA) to create the model is described for the engine test. Finally, the experimental results are analysed and it is shown that the proposed approach combining RCGA and RBF models is well suited for the engine test data modelling task.

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Дисертації з теми "REAL CODED GENETIC ALGORITHM (RCGA)"

KUMAR, NEERAJ. "DESIGNING OF MARKET MODEL, EFFECTIVE PRICE FORECASTING TOOL AND BIDDING STRATEGY FOR INDIAN ELECTRICITY MARKET." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18910.

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The Development scenario for renewable energy across the globe is changing rapidly in terms of capacity addition and grid interconnection. Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power growing enormously across the globe. Solar energy is widely escalating in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for exact electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. As renewable energy penetration into the grid is enhancing swiftly. An appropriate market model addressing the issues of related to renewable energy specially wind and solar is necessary. A novel solar energy-based market model is proposed for state level market along with the operating mechanism. The different component associated with grid and their functionality in the operation of grid is discussed. Challenges and possible solutions are addressed to implement the market model. Energy trading plays a crucial role in the economic growth of country. Renewable energy trading opens a new avenue for the economic growth. India is blessed with a rich solar energy resources, the solar power producers tapped the potential of solar up to appreciable extent, but due to lack of trading models and specific regulatory mechanism in context of renewable energy generation is main hurdle in competition among generators. Various market model developed for solar energy trading at state level electricity along with their trading mechanism is presented. Also features of the models are also addressed. xiii A Rigorous literature review on price forecasting is conducted with focus on impact of solar and wind energy on electricity price. The data of Australia electricity market is collected for price forecasting. The correlation among the inputs for price is calculated using correlation coefficient formula and selected the highly corelated input with price. Artificial Neural Network (ANN) is implemented to forecast the price by using historical data. The price is predicted for January to June month and weekly forecast of price for the same month is executed. The minimum MAPE is 1.94 for April month and 1.03 for third week of January. The research work is continued to investigate the impact of solar and wind energy on electricity price. The Long short-term memory (LSTM) is designed to forecast the electricity price considering the solar power penetration. The raw data of Austria market consists of actual day ahead load, forecasted day ahead load, actual day ahead price and actual solar generation is used. The reliability of forecasting model is analyzed by computation of confidence interval on MAPE. The research work is extended to investigate the impact of wind energy on electricity price. The Austria electricity market data is used for investigating the potential impact of wind energy on rice. The statistical analysis of the data is conducted for finding the suitability of the model. Decision tree model is designed and implemented and significant reduction in the forecasting accuracy of 5.802 is achieved for the data set using wind energy as input parameter. The future of solar energy in India is positive. The growth of solar energy in terms of capacity addition and grid interconnection programme is expanding day by day. To promote the solar energy trading in open market a suitable bidding mechanism must be designed for solar power producers. It becomes pertinent to design the bidding strategy for solar power producers to maximize their profit considering the uncertainty in the energy output. Hybrid Particle Swarm Optimization – Gravitational Search Algorithm (HPSO - GSA) is proposed for designing the optimal bidding strategy for solar PV power producer for designed solar energy xiv based Indian electricity market. The objective function is designed considering the constraint of uncertainty and energy imbalance in price. The proposed algorithm shows highest profit when compared with Real Coded Genetic Algorithm (RCGA), Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA). In the light of continual renewable energy growth and grid interconnection, a novel solar energy-based electricity market model addressing the issues of solar energy is proposed to make the system effective and reliable. This novel market may fill the promise of providing electricity at competitive cost for all in India. The various market models are proposed for trading the solar energy in competitive market for maximum utilization of untapped potential of solar energy. The various trading models may be implemented based on the application and suitability. The electricity price forecasting is an important aspects of power system planning and for renewable energy interactive grid price forecasting is crucial task due its intermittent nature. ANN model is proposed for price forecasting and significant improvement in MAPE is reported for Australia electricity market data. Further the investigation has been done on the impact of solar energy generation on electricity price using machine learning techniques (DT, RF, LASSO, XGBOOST and LSTM). The LSTM model accuracy is good in price forecasting with consideration of solar energy as input parameter. The investigation is extended for impact of wind energy on electricity price and Decision tree model accuracy is superior as compared to RF, LASSO, LR, SVR and DNN model. The bidding strategy for the designed solar based electricity model is proposed using HPSO-GSA method and profit calculation has been done for solar PV producers on real time data. The maximized profit has been obtained through HSPO-GSA method for two different sets of datasets.

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Dyer, John David Hartfield Roy J. "Aerospace design optimization using a real coded genetic algorithm." Auburn, Ala, 2008. http://repo.lib.auburn.edu/EtdRoot/2008/SPRING/Aerospace_Engineering/Thesis/Dyer_John_31.pdf.

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Hussain, M. S. "Real-coded genetic algorithm particle filters for high-dimensional state spaces." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1426733/.

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This thesis successfully addresses the issues faced by particle filters in high-dimensional state-spaces by comparing them with genetic algorithms and then using genetic algorithm theory to address these issues. Sequential Monte Carlo methods are a class of online posterior density estimation algorithms that are suitable for non-Gaussian and nonlinear environments, however they are known to suffer from particle degeneracy; where the sample of particles becomes too sparse to approximate the posterior accurately. Various techniques have been proposed to address this issue but these techniques fail in high-dimensions. In this thesis, after a careful comparison between genetic algorithms and particle filters, we posit that genetic algorithm theoretic arguments can be used to explain the working of particle filters. Analysing the working of a particle filter, we note that it is designed similar to a genetic algorithm but does not include recombination. We argue based on the building-block hypothesis that the addition of a recombination operator would be able to address the sample impoverishment phenomenon in higher dimensions. We propose a novel real-coded genetic algorithm particle filter (RGAPF) based on these observations and test our hypothesis on the stochastic volatility estimation of financial stocks. The RGAPF successfully scales to higher-dimensions. To further strengthen our argument that whether building-block-hypothesis-like effects are due to the recombination operator, we compare the RGAPF with a mutation-only particle filter with an adjustable mutation rate that is set to equal the population-to-population variance of the RGAPF. The latter significantly and consistently performs better, indicating that recombination is having a subtle and significant effect that may be theoretically explained by genetic algorithm theory. After two successful attempts at validating our hypothesis we compare the performance of the RGAPF using different real-recombination operators. Observing the behaviour of the RGAPF under these recombination operators we propose a mean-centric recombination operator specifically for high-dimensional particle filtering. This recombination operator is successfully tested and compared with benchmark particle filters and a hybrid CMA-ES particle filter using simulated data and finally on real end-of-day data of the securities making up the FTSE-100 index. Each experiment is discussed in detail and we conclude with a brief description of the future direction of research.

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Phan, Thanh Duoc. "Design optimisation of steel portal frames using real-coded niching genetic algorithm." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602785.

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This thesis is concerned with the design optimization of single-storey steel portal frame buildings. In the UK, such buildings account for 90% of all single-storey buildings and 50% of all constructional steelwork used. Two different types of steel portal frames are considered: conventional hot-rolled steel portal frames, which can achieve spans of up to 50 m, and cold-formed steel frames, which while less popular in the UK, may be more efficient for spans around 12 m. A real-coded niching genetic algorithm is used for the purposes of the design optimization. By adopting a niching strategy, the diversity of the population is effectively maintained and so increases the probability in searching for the optimum solution in the design space. The efficiency of the real-coded niching genetic algorithm is demonstrated through design examples of both hot-rolled steel and cold-formed steel portal frames. For the design optimization of hot-rolled steel portal frames, the optimization algorithm is used to minimize the material cost of the portal frame, per square m on plan, taking into account both the hot-rolled steel cross-sections and the eaves haunch size. In all cases, a frame spacing of 6 m is adopted. Both ultimate and serviceability limit states are considered, with deflection limits recommended by the Steel Construction Institute. It is shown that serviceability deflections govern for the design of a 50 m span portal frame, where material costs increase by 60% compared to an ultimate limit state design only. For small span frame, i.e., span of 10m, material cost only increases by 19%. For the design optimization of the cold-formed steel portal frame, the same algorithm is applied to minimize the material cost of the main frame members. In addition, frame spacings of both 4 m and 6 m are considered. For the case of a 12 m span frame, with rigid joints, it is shown that the frame design is not sensitive to serviceability deflections and that the frame is 24% cheaper (in terms of material costs per square m) than using hot-rolled steel. When the effects of semi-rigid joints and stressed-skin action are included, it is shown that the cost of members is further reduced by 32%.

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Lao, Seng Kin. "Computer-aided analysis for combined building services drawings using Real-coded Genetic Algorithm." Thesis, University of Macau, 2001. http://umaclib3.umac.mo/record=b1446119.

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Darwish, Mohammed. "Lot-sizing and scheduling optimization using genetic algorithm." Thesis, Högskolan i Skövde, Institutionen för ingenjörsvetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17045.

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Simultaneous lot-sizing and scheduling problem is the problem to decide what products to be produced on which machine and in which order, as well as the quantity of each product. Problems of this type are hard to solve. Therefore, they were studied for years, and a considerable number of papers is published to solve different lotsizing and scheduling problems, specifically real-case problems. This work proposes a Real-Coded Genetic Algorithm (RCGA) with a new chromosome representation to solve a non-identical parallel machine capacitated lot-sizing and scheduling problem with sequence dependent setup times and costs, machine cost and backlogging. Such a problem can be found in real world production line at furniture manufacturer in Sweden. Backlogging is an important concept in this problem, and it is often ignored in the literature. This study implements three different types of crossover; one of them has been chosen based on numerical experiments. Four mutation operators have been combined together to allow the genetic algorithm to scan the search area and maintain genetic diversity. Other steps like initializing of the population and a reinitializing process have been designed carefully to achieve the best performance and to prevent the algorithm from trapped into the local optimum. The proposed algorithm is implemented and coded in MATLAB and tested for a set of standard medium to large-size problems taken from the literature. A variety of problems were solved to measure the impact of different characteristics of problems such as the number of periods, machines, and products on the quality of the solution provided by the proposed RCGA. To evaluate the performance of the proposed algorithm, the average deviation from the lower bound and runtime for the proposed RCGA are compared with three other algorithms from the literature. The results show that, in addition to its high computational speed, the proposed RCGA outperforms the other algorithms for non-identical parallel machine problems, while it is outperformed by the other algorithms for problems with the more identical parallel machine. The results show that the different characteristics of problem instances, like increasing setup cost, and size of the problem influence the quality of the solutions provided by the proposed RCGA negatively.

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Lin, Chun-Hung, and 林俊宏. "Adaptive Real-coded Genetic Algorithm for Motor System Identification." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/35255717604174019114.

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碩士
國立高雄第一科技大學
電機工程研究所碩士班
102
In this paper, the main objective is to identify the parameters of motors, which includes a BLDC motor and an induction motor. The real-coded genetic algorithm (RGA) is adopted to identify all parameters of motors, and the standard genetic algorithm (SRGA) and adaptive genetic algorithm (ARGA) are compared in the rotational angular speeds and fitness values, which are the inverse of square difference of angular speeds. From numerical simulations and experimental results, it is found that the SRGA and ARGA are feasible, the ARGA can effectively solve the problems of slow convergent speed and premature phenomenon, and the ARGA is more accurate in identifying system’s parameters than the SRGA. From the comparisons of ARGAs in identifying parameters of motors, the best ARGA method is obtained and could be applied to any other areas of expertise.

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Gibbs, Matthew S. "Real-coded genetic algorithm parameter setting for water distribution system optimisation." 2008. http://hdl.handle.net/2440/49644.

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The management of Water Distribution Systems (WDSs) involves making decisions about various operations in the network, including the scheduling of pump operations and setting of disinfectant dosing rates. There are often conflicting objectives in making these operational decisions, such as minimising costs while maximising the quality of the water supplied. Hence, the operation of WDSs can be very difficult, and there is generally considerable scope to improve the operational efficiency of these systems by improving the associated decision making process. In order to achieve this goal, optimisation methods known as Genetic Algorithms (GAs) have been successfully adopted to assist in determining the best possible solutions to WDS optimisation problems for a number of years. Even though there has been extensive research demonstrating the potential of GAs for improving the design and operation of WDSs, the method has not been widely adopted in practice. There are a number of reasons that may contribute to this lack of uptake, including the following difficulties: (a) developing an appropriate fitness function that is a suitable description of the objective of the optimisation including all constraints, (b) making decisions that are required to select the most appropriate variant of the algorithm, (c) determining the most appropriate parameter settings for the algorithm, and (d) a reluctance of WDS operators to accept new methods and approaches. While these are all important considerations, the correct selection of GA parameter values is addressed in this thesis. Common parameters include population size, probability of crossover, and probability of mutation. Generally, the most suitable GA parameters must be found for each individual optimisation problem, and therefore it might be expected that the best parameter values would be related to the characteristics of the associated fitness function. The result from the work undertaken in this thesis is a complete GA calibration methodology, based on the characteristics of the optimisation problem. The only input required by the user is the time available before a solution is required, which is beneficial in the WDS operation optimisation application considered, as well as many others where computationally demanding model simulations are required. Two methodologies are proposed and evaluated in this thesis, one that considers the selection pressure based on the characteristics of the fitness function, and another that is derived from the time to convergence based on genetic drift, and therefore does not require any information about the fitness function characteristics. The proposed methodologies have been compared against other GA calibration methodologies that have been proposed, as well as typical parameter values to determine the most suitable method to determine the GA parameter values. A suite of test functions has been used for the comparison, including 20 complex mathematical optimisation problems with different characteristics, as well as realistic WDS applications. Two WDS applications have been considered: one that has previously been optimised in the literature, the Cherry Hills-Brushy Plains network; and a real case study located in Sydney, Australia. The optimisation problem for the latter case study is to minimise the pumping costs involved in operating the WDS, subject to constraints on the system, including minimum disinfectant concentrations. Of the GA calibration methods compared, the proposed calibration methodology that considered selection pressure determined the best solution to the problem, producing a 30% reduction in the electricity costs for the water utility operating the WDS. The comparison of the different calibration approaches demonstrates three main results: 1. that the proposed methodology produced the best results out of the different GA calibration methods compared; 2. that the proposed methodology can be applied in practice; and 3. that a correctly calibrated GA is very beneficial when solutions are required in a limited timeframe.
http://proxy.library.adelaide.edu.au/login?url= http://library.adelaide.edu.au/cgi-bin/Pwebrecon.cgi?BBID=1325448
Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2008

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Gibbs, Matthew S. "Real-coded genetic algorithm parameter setting for water distribution system optimisation." Thesis, 2008. http://hdl.handle.net/2440/49644.

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The management of Water Distribution Systems (WDSs) involves making decisions about various operations in the network, including the scheduling of pump operations and setting of disinfectant dosing rates. There are often conflicting objectives in making these operational decisions, such as minimising costs while maximising the quality of the water supplied. Hence, the operation of WDSs can be very difficult, and there is generally considerable scope to improve the operational efficiency of these systems by improving the associated decision making process. In order to achieve this goal, optimisation methods known as Genetic Algorithms (GAs) have been successfully adopted to assist in determining the best possible solutions to WDS optimisation problems for a number of years. Even though there has been extensive research demonstrating the potential of GAs for improving the design and operation of WDSs, the method has not been widely adopted in practice. There are a number of reasons that may contribute to this lack of uptake, including the following difficulties: (a) developing an appropriate fitness function that is a suitable description of the objective of the optimisation including all constraints, (b) making decisions that are required to select the most appropriate variant of the algorithm, (c) determining the most appropriate parameter settings for the algorithm, and (d) a reluctance of WDS operators to accept new methods and approaches. While these are all important considerations, the correct selection of GA parameter values is addressed in this thesis. Common parameters include population size, probability of crossover, and probability of mutation. Generally, the most suitable GA parameters must be found for each individual optimisation problem, and therefore it might be expected that the best parameter values would be related to the characteristics of the associated fitness function. The result from the work undertaken in this thesis is a complete GA calibration methodology, based on the characteristics of the optimisation problem. The only input required by the user is the time available before a solution is required, which is beneficial in the WDS operation optimisation application considered, as well as many others where computationally demanding model simulations are required. Two methodologies are proposed and evaluated in this thesis, one that considers the selection pressure based on the characteristics of the fitness function, and another that is derived from the time to convergence based on genetic drift, and therefore does not require any information about the fitness function characteristics. The proposed methodologies have been compared against other GA calibration methodologies that have been proposed, as well as typical parameter values to determine the most suitable method to determine the GA parameter values. A suite of test functions has been used for the comparison, including 20 complex mathematical optimisation problems with different characteristics, as well as realistic WDS applications. Two WDS applications have been considered: one that has previously been optimised in the literature, the Cherry Hills-Brushy Plains network; and a real case study located in Sydney, Australia. The optimisation problem for the latter case study is to minimise the pumping costs involved in operating the WDS, subject to constraints on the system, including minimum disinfectant concentrations. Of the GA calibration methods compared, the proposed calibration methodology that considered selection pressure determined the best solution to the problem, producing a 30% reduction in the electricity costs for the water utility operating the WDS. The comparison of the different calibration approaches demonstrates three main results: 1. that the proposed methodology produced the best results out of the different GA calibration methods compared; 2. that the proposed methodology can be applied in practice; and 3. that a correctly calibrated GA is very beneficial when solutions are required in a limited timeframe.
Thesis (Ph.D.) - University of Adelaide, School of Civil, Environmental and Mining Engineering, 2008

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Kung, Yen-Shiung, and 龔彥勲. "Motion planning for redundant robots using a CUDA-based real-coded genetic algorithm." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/7982t2.

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碩士
國立臺北科技大學
自動化科技研究所
100
Motion planning of redundant robot manipulators is a difficult problems encountered in the field of robotics. In this paper, we use Forward Kinematic and Optimal Control to avoid singularity problem in motion planning that use Inverse Kinematic to compute. It is a large quantity of computing in searching optimal result process, due to this reason, we use Genetic Algorithm and Particle Swarm Optimization Algorithm to reduce complexity and use parallel computing architecture of GPU(CUDA) to improve computing speed.

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Частини книг з теми "REAL CODED GENETIC ALGORITHM (RCGA)"

Bhosale, K. C., and P. J. Pawar. "Material Flow Optimisation in a Manufacturing Plant by Real-Coded Genetic Algorithm (RCGA)." In Studies in Quantitative Decision Making, 99–111. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-5820-4_5.

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Wang, Zhonglai, Jingqi Xiong, Qiang Miao, Bo Yang, and Dan Ling. "New Hybrid Real-Coded Genetic Algorithm." In Lecture Notes in Computer Science, 1221–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_151.

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Munteanu, Cristian, and Vasile Lazarescu. "Improving Mutation Capabilities in a Real-Coded Genetic Algorithm." In Evolutionary Image Analysis, Signal Processing and Telecommunications, 138–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/10704703_11.

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Chauhan, Pinkey. "Real Coded Genetic Algorithm for Selecting Optimal Machining Conditions." In Algorithms for Intelligent Systems, 89–99. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1528-3_8.

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Devi Arockia Vanitha, C., D. Devaraj, and M. Venkatesulu. "Real Coded Genetic Algorithm for Development of Optimal G-K Clustering Algorithm." In Swarm, Evolutionary, and Memetic Computing, 264–74. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20294-5_23.

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Singh, Prabhash Kumar. "A Modified Real-Coded Extended Line Crossover for Genetic Algorithm." In Social Transformation – Digital Way, 702–16. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1343-1_58.

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Nadakuditi, Gouthamkumar, U. Mohan Rao, Venkateswararao Bathina, and Srihari Pandi. "Economic Load Dispatch in Microgrids Using Real-Coded Genetic Algorithm." In Soft Computing in Data Analytics, 377–86. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0514-6_38.

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Hamada, Naoki, Jun Sakuma, Shigenobu Kobayashi, and Isao Ono. "Functional-Specialization Multi-Objective Real-Coded Genetic Algorithm: FS-MOGA." In Parallel Problem Solving from Nature – PPSN X, 691–701. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87700-4_69.

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Li, Xiaodong. "A Real-Coded Predator-Prey Genetic Algorithm for Multiobjective Optimization." In Lecture Notes in Computer Science, 207–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36970-8_15.

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Deep, Kusum, Shashi, and V. K. Katiyar. "A New Real Coded Genetic Algorithm Operator: Log Logistic Mutation." In Advances in Intelligent and Soft Computing, 193–200. India: Springer India, 2012. http://dx.doi.org/10.1007/978-81-322-0487-9_19.

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Тези доповідей конференцій з теми "REAL CODED GENETIC ALGORITHM (RCGA)"

Singh, Gurjot, Neeraj Gupta, and Mahdi Khosravy. "New crossover operators for real coded genetic algorithm (RCGA)." In 2015 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). IEEE, 2015. http://dx.doi.org/10.1109/iciibms.2015.7439507.

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Minuchehr, Hamid, Ahmad Zolfaghari, Peymaan Makarachi, and Ali Noroozy. "Using a Real Coded Genetic Algorithm to Obtain the Optimal Parameters of a Cascade of Gas Centrifuge." In 17th International Conference on Nuclear Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/icone17-75926.

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A minimum total number of centrifuges can be taken as an optimization criterion in designing a cascade of centrifuges. The separable power of a centrifuge of given geometry (rotor height and radius) and specified peripheral speed (the highest that materials of construction can withstand) depends upon the internal variables controlling the component drives and upon the operating variables, which are the cut θ, and the throughput, L. To enhance separative power of a gas centrifuges cascade, one needs to optimize all parameters. In this paper, the Real Coded Genetic Algorithm, RCGA, is implemented. As an example of the method, the dependence of separation factor, α to variables cut and throughput is assumed as a typical function. The outcomes of the method are in good agreement with published data.

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Shi, Zhen, and Peter Sandborn. "Modeling Test, Diagnosis, and Rework Operations and Optimizing Their Location in General Manufacturing Processes." In ASME 2003 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2003. http://dx.doi.org/10.1115/detc2003/dfm-48145.

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This paper presents a test, diagnosis, and rework analysis model for use in manufacturing process modeling. The approach includes a model of functional test operations characterized by fault coverage, false positives, and defects introduced in test, in addition to rework and diagnosis (diagnostic test) operations that have variable success rates and their own defect introduction mechanisms. The model accommodates multiple rework attempts on a product instance. The model is applied within a framework for optimizing the location(s) and characteristics (fault coverage/test cost, rework success rate/rework cost) of Test/Diagnosis/Rework (TDR) operations in a general manufacturing process. A new search algorithm called Waiting Sequence Search (WSS) is applied to traverse a general process flow to perform the cumulative calculation of a yielded cost objective function. Real-Coded Genetic Algorithms (RCGAs) are used to perform a multi-objective optimization that minimizes yielded cost. An example of a general complex process flow is used to demonstrate the feasibility of the algorithm.

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Chakraborty, S., S. Phadke, and R. K. Verma. "AVA Inversion Using Real-Coded Genetic Algorithm." In 66th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2004. http://dx.doi.org/10.3997/2214-4609-pdb.3.p307.

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Turnea, Marius, Dragos Arotaritei, and Robert Fuior. "INTELLIGENT ALGORITHMS WITH SELECTION OF HYPERPARAMETERS FOR E-HEALTH APPLICATIONS POWERED BY 5G WIRELESS NETWORKS." In eLSE 2020. University Publishing House, 2020. http://dx.doi.org/10.12753/2066-026x-20-205.

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Different from previous generations, the 5G networks has new capabilities due to service-based architecture model and virtualization. The successful broadband networks must be able to handle the growth in the data traffic. The e-Health networks has additional issues as the continuous monitoring of patients suffering from chronic diseases (non-communicable diseases). The wearable devices used for monitoring are supposed to be used for balneo-physio-kinetotherapy (including the body gait index calculation) in the future and this will require and increasing traffic as users and data for 5G networks. Medical data and biomedical data are usually very large (especially for medical images) and the traffic can be critical in some situation when in order to take a decision due to alarms from generated by medical emergency when the data should be provided very fast to the physicians (hospitals, or clinics). An architecture for smart e-Health monitoring including the management of big database open the opportunity to use intelligent algorithm for complex problems, machine learning and artificial intelligence. The possibility to use of three algorithms in simulation and simulators for e-Health 5G wireless network is investigate in this paper. One of the key requirement is low energy consumption due to number of antenna elements at the access points and number of user terminals. The problem optimization address to a mix agglomeration: dense urban area along with a set of dispersed locations in a rural area. The network planning is defined as optimization problem of configuration that depends on BS (Base Station) location and transmission power but as novelty, the constraints due to inclusion of rural area are also included in feasible solution. The constraints refer to two situations: the relief (that can be natural zone) or imposed black zone due external factors. Three algorithms are examined: Real-coded Genetic Algorithm for Variable Population - RCGAV), NSGA - II and Gossip, applied to modelling and optimization of power consumption in wireless access networks. Scenarios with simulation of the traffic between the client and the server are taken in to account using known models of distribution: Poisson, Pareto, and Weibull.

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Kumar, Sanjeev, Manoj Thakur, Balasubramanian Raman, and N. Sukavanam. "Stereo camera calibration using real coded genetic algorithm." In TENCON 2008 - 2008 IEEE Region 10 Conference (TENCON). IEEE, 2008. http://dx.doi.org/10.1109/tencon.2008.4766590.

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Zhang, Guo-li, Geng-yin Li, and Jian-wei Ma. "Real-Coded Genetic Algorithm for Constrained Optimization Problem." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259005.

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HajAbedi, Z. "A real-coded genetic algorithm for constructive induction." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983191.

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Chen Lan and Li Fei. "A real-coded chaotic immune quantum genetic algorithm." In 2010 International Conference on Future Information Technology and Management Engineering (FITME). IEEE, 2010. http://dx.doi.org/10.1109/fitme.2010.5655686.

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Fossati, Luca, Pier Luca Lanzi, Kumara Sastry, David E. Goldberg, and Osvaldo Gomez. "A Simple Real-Coded Extended Compact Genetic Algorithm." In 2007 IEEE Congress on Evolutionary Computation. IEEE, 2007. http://dx.doi.org/10.1109/cec.2007.4424491.

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