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As an optimization technique, a modified Genetic Algorithm was used to find the optimum points.
This study aims at introducing a problem-specific modified Genetic Algorithm (GA) approach for optimal well placement in oil fields.
In the proposed work, FIS has been optimized using three approaches namely: gradient descent (GD), genetic algorithm (GA) and modified genetic algorithm (MGA).
Optimal quality control of drying process of baker's yeast in large scale batch fluidized bed dryer is presented using neural network based models and modified genetic algorithm (GA).
The techniques are composed of cross combination between two modified nonlinear optimisation algorithms (modified Genetic Algorithm and modified Levenberg Marquardt) adapted for battery cell parameter estimation.
Manufacturing process planning for multiple parts manufacturing is cast as a hard optimization problem for which a modified genetic algorithm is proposed in this paper.
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In this paper, a novel hybrid learning which consists of self-clustering algorithm (SCA) and modified genetic algorithms (MGA) is proposed for solving the classification problems.
In addition, with the developed algorithm structure, variable-length chromosome, modified genetic operations, and algorithm parameters tuned by Taguchi Experimental Design, the proposed GA can deal with the integrated optimisation problem very effectively.
In the second part, we develop a modified genetic-based algorithm for solving the mc-WMP.
We then modified the Genetic Algorithm (GA) based task remapping and proposed two other task migration algorithms: Simple Exchange (SE) and Benefit Assess (BA).
In addition, regarding BOSC problems solving, this study proposes a modified Pareto genetic algorithm (mPaGA) to improve the solution quality through revision of crossover and mutation operations.
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