MATLAB Implementation of Real-Coded Genetic Algorithm with Elite Preservation

This code provides a MATLAB implementation of a real-coded genetic algorithm. In genetic algorithms, real-number encoding serves as a widely adopted approach for solving various optimization problems. The algorithm incorporates elite preservation strategy alongside roulette wheel selection to maintain population diversity and improve evolutionary effectiveness. The implementation uses intermediate recombination for crossover operations, where new individuals are generated by swapping intermediate points between two parent individuals. This method effectively overcomes encoding length constraints while enhancing the algorithm's search capability and convergence speed. Key implementation features include: population initialization with real-valued chromosomes, fitness evaluation through objective function computation, and generational evolution with controlled mutation rates. Through this MATLAB routine, users can gain deeper understanding of real-coded genetic algorithm principles and applications, learning how to adapt the algorithm for solving custom optimization problems. The code structure demonstrates practical handling of selection pressure, crossover probability parameters, and termination conditions for real-world optimization scenarios.