In this paper we discuss how standard genetic algorithm (SGA) could be applied to get an optimal cricket team from a set of 50 national level players. Since a cricket team needs to be more flexible, balanced and diverse, we defined fitness function to check the optimality of the team, and how the optimality changes with the change in gene composition of a chromosome. The proposed method takes into account several important factors that affect a team combination like: No. of pacers and spinners, composition of left hander and right hander, partnership records etc.. Starting from a random initial population, the normal SGA operations (selection, reproduction and mutation) to get the next population set and the process is iterated till an optimal team is produced or a fixed number of times. The proposed system is made very generic and it can be applied for any game by just modifying the fitness function.

Abstract-This paper is aimed at demonstrating a genetic algorithm method and applying it to predict the water quality of reservoir in Taiwan island using remote sensing data. Genetic algorithms will be combined with operation tree (GAOT) to find the relationships between input and output data. A fittest function type will be obtained automatically from this method. The advantages of GA are global optimization, nonlinearity, flexibility and parallelism. In the current case study, GA is used to construct the relationship between algae concentration and Landsat sensor data. The results show that the model has better performance than the traditional LN transform of linear regression method, and similar performance compared with back-propagation neural network (BPNN) method.Index Terms-Genetic algorithm, Landsat, LN transform linear regression, back-propagation neural network, operation tree.

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