MATLAB Code Implementation of Genetic Algorithm for Nonlinear Integer Programming

This MATLAB program implements a genetic algorithm to solve nonlinear integer programming problems. Genetic algorithms are optimization techniques that mimic natural selection and genetic mechanisms to search for optimal solutions. The implementation typically includes key components such as population initialization using integer encoding, fitness evaluation through objective function calculation, selection operators like tournament selection, crossover operations with specialized integer handling, and mutation mechanisms preserving integer constraints. These algorithms are widely applicable in engineering design, economic modeling, and scheduling problems where traditional methods may struggle. For nonlinear integer programming, the genetic algorithm helps identify integer solutions that minimize objective functions while satisfying linear constraints, particularly effective for large-scale complex problems where solution spaces are discontinuous or non-convex. The MATLAB implementation leverages built-in functions for efficient matrix operations and provides customizable parameters for population size, mutation rates, and termination criteria to balance exploration and exploitation during optimization.