MATLAB Implementation of Genetic Algorithm

Genetic Algorithm is a global optimization method that simulates natural selection and genetic mechanisms. When implementing in MATLAB, it typically includes the following core modules: First is the population initialization phase, where candidate solutions are created through random generation or specific rules using functions like rand() or randi(). The algorithm then enters an iterative loop where each generation performs fitness evaluation (calculating objective function values), selection operations (such as roulette wheel selection using cumulative probabilities or tournament selection), crossover recombination (single-point/multi-point crossover implemented with array slicing), and mutation operations (bit flipping for binary encoding or Gaussian perturbation for real-valued encoding using randn()). Encoding methods can employ binary strings or real-number encoding, where the design of the fitness function directly influences the algorithm's convergence direction. A typical implementation includes elitism strategy (preserving best individuals using sort() function) to prevent loss of excellent solutions, and terminates computation through stopping conditions (such as maximum iteration counters or convergence thresholds monitored via while/for loops). This algorithm is particularly suitable for complex nonlinear problems, but requires careful attention to parameter tuning (crossover rate and mutation rate typically set between 0.6-0.9 and 0.01-0.1 respectively) as they critically impact performance.