Genetic Algorithms for Constructing Effective Nuclear Shell-Model Hamiltonians

The nuclear shell model is one of the most adopted many-body methods for the description of atomic nuclei whose main ingredient is the effective Hamiltonian. One of the approaches widely used to derive it is of phenomenological type, where its matrix elements are considered parameters to be fixed to reproduce the experimental data. However, the number of parameters as well as the number of experimental data dramatically increases with the mass of the nuclei under investigation and the commonly adopted procedures such as least-square fitting become computationally prohibitive. Therefore, there is a strong emergence in finding alternative approaches to construct effective Hamiltonians for heavy nuclei. To pave the way in this direction, the proposed work exploits for the very first time Genetic Algorithms. Indeed, their ability to simultaneously examine and manipulate sets of possible solutions could be crucial to deal with the above issues. The suitability of Genetic Algorithms in computing effective Hamiltonians is evaluated experimentally for the p - shell interaction, where the obtained results, without any physical constraint on the parameters, outperform the current widely-adopted description represented by the Cohen and Kurath solution.