Application of Genetic Algorithm for optimization of fuel management in nuclear reactors

Genetic algorithms for nuclear reactor fuel load and reload optimization problems

A genetic algorithm for online-scheduling of a multiproduct polymer batch plant

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.

This paper innovates, within the context of preventive maintenance planning, the application of Genetic Algorithm, as a modern powerful optimization tool, in minimum downtime strategies, for optimal part replacement. Therefore, a brief account of Genetic Algorithm is given and a computer program is written to aid the solution of such problems by Genetic Algorithm. Then, the program is used to solve a numerical example, concerning an equipment with Gaussian failure characteristics. The results obtained by Genetic Algorithm and the ordinary methods are compared to show that exactly the same results can be approached by the application of Genetic Algorithm. Genetic Algorithm and maintenance planning, both being subjects of statistical nature, can be combined to provide a vast area of interesting research work and, hence, some guidelines are suggested for further research.

Application of genetic algorithm in modelling and optimization of cellulase production

Optimization of parameters of pulse current gas tungsten arc welding using non conventional techniques

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

This paper considers genetic algorithm to process logistics path optimization. Genetic algorithms are significant in location issues, distribution issues, scheduling issues, transportation issues, and layout issues. A genetic algorithm for solving this problem is constructed base on the mathematical model of logistics distribution path optimization problem. The calculation results show that using genetic algorithm to optimize the logistics distribution path can easily and effectively obtain the optimal solution or approximate optimal solution.

The following article deals with up-to-date field of optimization and genetic algorithms use in the production process control. Theory of optimization is mentioned in the first part of this paper. The second part of this article is dedicated to genetic algorithms. Genetic algorithms are characterized by their robustness – the ability to solve difficult optimization or the tasks in which we need to decide on something.The third part of the paper focuses on practical demonstrations of two examples. The first example is on determining the optimal production. The second example is on application of genetic algorithms in process planning. Matlab and Evolver software tools were used for the data compution. Data preparation for Evolver was done in MS Excel. But the model building is only one step in knowledge discovery. The best model is often found after building models of several different types, or by trying different technologies or algorithms.

Application of Data-Driven technology in nuclear Engineering: Prediction, classification and design optimization

The application of genetic algorithms in behavioural ecology, illustrated with a model of anti-predator vigilance

An investigation into the application of neural networks, fuzzy logic, genetic algorithms, and rough sets to automated knowledge acquisition for classification problems

Application of genetic algorithm and greedy stepwise to select input variables in classification tree models for the prediction of habitat requirements of Azolla filiculoides (Lam.) in Anzali wetland, Iran

In-pile testing of ITER first wall mock-ups at relevant thermal loading conditions in the LVR-15 nuclear research reactor

This paper presents comparison of the optimum performance characteristics of four different bearing configurations. An attempt has been made to find out the effect of four different bearing configurations of hydrodynamic journal bearing by changing groove locations. Various groove angles that have been considered are 10°, 20°, and 30°. The Reynolds equation is solved numerically in a finite difference grid satisfying the appropriate boundary conditions. Four optimum performance parameters considered viz non-dimensional load carrying capacity, flow coefficient, friction variable, and mass parameter. Optimum configuration of bearings ensures best flow, load and stability, and least friction of the bearings. Genetic algorithm (GA) for multi-objective function has been used for optimum performance parameter comparison of the bearings. Flow coefficient value is found higher for elliptical bearing, and optimum value of non-dimensional load carrying capacity mass parameter found to be the highest for four-lobe bearing.