Novel core physics heuristics in advanced genetic algorithms for in-core fuel management

In this work, modern and improved genetic algorithms are implemented for the problem of the in-core fuel management. This is achieved using state-of-the-art selection and crossover operators and novel fitness function constructions, e.g., rank selection or tournament selection instead of the traditional roulette wheel selection operator; improved crossover and mutation operators by considering the chromosomes as permutations (which is a specific feature of the loading pattern problem); and the 'stage fitness function' that separates the different objectives of the optimization. Another novel feature of the algorithm is the consideration of the geometric nature of the problem and the desired loading pattern solutions. A new geometric crossover is developed to utilize this geometric knowledge and is used with good results. The new algorithm is implemented and applied to two benchmark problems and used to study the effect of boundary conditions on the symmetry of the obtained best solutions and the trade-off relationship between the studied objectives of the optimization, e.g., k_eff and PPF. (authors)