Genetic Algorithm for Nonlinear Integer Programming

This MATLAB program implements a genetic algorithm specifically designed for solving nonlinear integer programming problems. It serves as a powerful tool for addressing complex optimization challenges across multiple domains. The genetic algorithm mimics natural evolutionary processes by simulating genetic inheritance and evolution to find optimal solutions. Nonlinear integer programming involves optimization problems where both the objective function and constraints contain nonlinear terms and integer variables, commonly encountered in engineering, economics, and management fields. The program incorporates key genetic algorithm components including: - Binary or integer-based chromosome encoding for representing integer variables - Fitness proportional selection or tournament selection mechanisms - Specialized crossover operators (such as single-point or uniform crossover) adapted for integer constraints - Mutation operations with probability control to maintain integer feasibility - Constraint handling techniques for nonlinear constraints By integrating genetic algorithms with nonlinear integer programming, this implementation effectively solves complex optimization problems while ensuring integer variable requirements. The algorithm iteratively evolves a population of potential solutions through selection, crossover, and mutation operations, gradually converging toward optimal or near-optimal solutions. The program includes termination criteria based on maximum generations or convergence thresholds to ensure computational efficiency.