Predictive Modeling of Engineering Placement Outcomes Using Genetic Algorithms

It is difficult to predict where engineering graduates will be employed due to various factors including market needs, technical skills, personal qualities, and academic achievement. Traditional techniques often produce less-than-ideal results because they are unable to fully capture the complex interactions between these factors. In this paper, we provide a novel method to tackle this problem by using genetic algorithm (GA) placement prediction capabilities. Genetic algorithms mimic the principles of evolution to find optimal solutions in a large solution space. The process of natural selection inspires them. GA effectively searches the solution space by listing possible solutions as chromosomes and performing selection, crossover, and mutation operations to identify optimal solutions.