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This paper presents a new idea to solve the selective harmonic elimination (SHE) equations with non equal dc sources using Hybrid Newton Raphson Method (HNRM). This technique is a combination of genetic algorithm (GA) and Newton Raphson Method (NRM). The HNRM technique effectively eliminates the lower order harmonics from the output voltage of the converter. In this paper, it has been also shown that,...
With increasing complexity of electronic circuits, the design and optimization of electronic circuit needs to be automated with high degree of reliability and accuracy. In order to optimize hardware requirement of digital combinational circuits, evolutionary and innovative techniques need to be enforced at various levels such as gate level and device level. This paper presents the use of one of the...
This paper presented a genetic - fuzzy logic based Proportional-Integral-Derivative controller (GFPID) for automatic generation control of two area thermal-thermal power system. Genetic algorithm has been applied to simultaneously tune PID gains, membership functions and control rules of FPID controller to minimize the frequency deviations of the system against the load disturbances. An objective...
Fault tree analysis is a widely accepted technique to assess the probability and frequency of system failure in many industries. Traditionally statistical methods and boolean reductions is employed to analyze the fault tree. Even though the fault tree approach is commonly used for system reliability analysis, there are inherent limitations in terms of accuracy and computational efficiency. For the...
Mutation Testing is used as fault-based testing to overcome limitations of other testing approaches but it is recognized as expensive process. In mutation testing, a good test case is one that kills one or more mutants, by producing different mutant output from the original program. Evolutionary algorithms have been proved its suitability for reducing the cost of data generation in different testing...
Regression testing is an expensive and frequently executed maintenance process used to revalidate modified software. Various problems are associated with regression testing such as regression test selection problem, coverage identification problem, test case execution problem, test case maintenance problem etc. In test selection problem, appropriate and effective test data is to be selected from the...
The parameter selection is very important for successful modelling of input-output relationship in a function approximation model. In this study, support vector machine (SVM) has been used as a function approximation tool for a price series and genetic algorithm (GA) has been utilised for optimisation of the parameters of the SVM model. Instead of using single time series, separate time series for...
All areas relating to telecommunications, electricity distribution, and gas pipeline require Topological optimization. It also has a major importance in the computer communication industry, when considering network reliability. In this paper, we have used GA with specialized encoding, initialization, local search operators with specially designed crossover operator called alternating crossover [21]...
Many real life problems require optimization of more than one objective functions, these problems are known as multi-objective optimization problems. Although many algorithms are available in literature to solve these types of problems [2, 3, 4, 5, 21] but they treat every objective function equally. Sometimes, on the basis of problem to be optimized, different objective functions can be assigned...
This paper presents a genetic algorithm approach to solve the "map colour problem". The map colour problem states that given any plane separated into regions, such as a political map of the states of a country, the regions may be coloured using two colours in such a way that no two adjacent regions receive the same colour. In this paper I have used genetic algorithm to find the solution...
The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and...
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