Abstract: Constraint-handling techniques and genetic operators are two crucial components in constrained multi-objective evolutionary algorithms (CMOEAs). Recent research in most of CMOEAs has ...

This project solves the diet optimization problem using a genetic algorithm. The goal is to find the most cost-effective combination of foods that meets all nutritional requirements. The diet problem ...

This project implements a genetic algorithm approach to solve the Resource-Constrained Project Scheduling Problem (SRCPSP), which is a well-known NP-hard optimization problem in operations research ...

Abstract: The flexible job shop scheduling problem (FJSP) is a prominent research focus in the field of manufacturing optimization. In practical production environments, machine and worker constraints ...

The optimisation of process planning has emerged as a pivotal aspect of modern manufacturing, where genetic algorithms (GAs) and hybrid techniques are leveraged to address the combinatorial complexity ...