This repository contains an implementation of the research paper by Metawa N. et all on optimizing bank lending decisions using genetic algorithms. The research paper can be found at https://www.sciencedirect.com/science/article/abs/pii/S0957417417301677?via%3Dihub
Also, a simulated annealing algorithm has been implemented and the results of both the algorithms are compared. It was found that the Genetic Algorithm performs better than the Simulated Annealing Algorithm.
To run the code, simply run the main.ipynb file. It calls functions from genetic_functions.py and simulated_annealing_functions.py
To understand the functioning of the algorithm, you can refer the project report which breaks down the algorithm into simple explanations. The GAMCC directory contains a sample output of the Genetic Algorithm and a sample progress graph showing the progress of the fitness function. Similar files can be found in the Simulated Annealing Directory