MATLAB Genetic Algorithm for Single-Objective Optimization of Binary Function Maximization

In this problem, we employ MATLAB's genetic algorithm to solve a single-objective optimization problem aimed at maximizing a binary function. Genetic algorithms are computational methods that simulate natural selection and genetic mechanisms, mimicking biological evolution processes to search for optimal solutions. For our optimization task, we implement key components including: 1) Population initialization using random number generation (rand function) to create candidate solutions; 2) Fitness evaluation through direct function calculation to assess solution quality; 3) Selection operations (e.g., tournament selection or roulette wheel selection) to choose parents based on fitness scores; 4) Crossover operations (typically using arithmetic crossover or heuristic crossover) to combine parent chromosomes; and 5) Mutation operations (with Gaussian or uniform mutation) to maintain population diversity.

Through iterative optimization with genetic algorithms, we progressively refine solutions to achieve better results. The MATLAB implementation involves proper problem modeling using function handles (@) for objective functions, parameter configuration including population size, crossover rate, and mutation probability, and termination criteria setting. This approach ensures accurate results while maintaining the core concepts from the original text.