Bi Objective

Bi-objective optimization tackles problems with two competing objectives, aiming to find solutions that represent the best trade-off between them. Current research focuses on developing efficient algorithms, such as variations of genetic algorithms and A*, to approximate the Pareto front—the set of optimal compromises—and applying these techniques to diverse fields including fair machine learning, routing optimization, and clinical data analysis. This approach is significant because it allows for more nuanced decision-making in complex systems where optimizing a single objective might neglect crucial constraints or secondary goals, leading to improved solutions in various practical applications.