Genetic Algorithm Implementation Using Real-Valued Encoding

This algorithm employs a genetic algorithm with real-valued encoding to identify maximum values for both constrained and unconstrained functions. The genetic algorithm is an optimization technique that mimics biological evolution processes to search for optimal solutions. In this implementation, real-valued encoding represents individuals within the solution space, while genetic operations including selection, crossover, and mutation generate new offspring populations. Key implementation aspects include: - Real-number chromosome representation for continuous parameter optimization - Fitness evaluation functions that handle both constrained and unconstrained scenarios - Selection mechanisms (such as tournament or roulette wheel selection) to preserve fit individuals - Arithmetic crossover operations for real-valued gene recombination - Mutation operators with controlled probability to maintain population diversity Through iterative generations, the genetic algorithm progressively improves solution quality by evolving populations toward optimal regions. The algorithm's exploration and convergence patterns can be monitored by plotting offspring quantities across generations, revealing how the algorithm navigates the search space. This visualization helps analyze population diversity maintenance and convergence rates. This robust optimization approach demonstrates particular effectiveness for complex multidimensional problems where traditional gradient-based methods may struggle with local optima or discontinuous search spaces. The real-valued encoding specifically suits continuous parameter optimization problems commonly encountered in engineering design and scientific computing applications.