Genetic Simulated Annealing Algorithm General Source Code with Population Size Specification

This general source code implementation for genetic simulated simulated annealing algorithm specifies population size, groups, populations, and initial temperature parameters. The algorithm combines biological genetic processes and metal annealing simulation to search for optimal solutions. The implementation consists of two core components: genetic algorithm operations and simulated annealing mechanics. The genetic algorithm component simulates biological evolution through crossover and mutation operations to generate new solutions, while fitness functions evaluate solution quality. In code implementation, this typically involves tournament selection or roulette wheel selection methods, with crossover rates controlling solution recombination and mutation rates introducing diversity. The simulated annealing component mimics metal cooling processes by probabilistically accepting inferior solutions to escape local optima. The Metropolis criterion is implemented through a probability function that decreases as temperature cools, allowing the algorithm to explore wider solution spaces initially while converging toward optimal solutions. During initialization, crucial parameters must be configured including population size (defining the number of candidate solutions), initial temperature (controlling solution acceptance probability), cooling rate, and stopping criteria. The iterative optimization process continuously refines solution quality through generations until termination conditions are met, such as maximum iterations or convergence thresholds. Key functions typically include: - Population initialization with random or heuristic-based solutions - Fitness evaluation using problem-specific objective functions - Temperature scheduling with geometric or logarithmic cooling schemes - Elite preservation strategies to maintain best solutions across generations Thus, the genetic simulated annealing algorithm represents a powerful hybrid optimization approach suitable for solving various complex problems with multimodal search spaces and strong global optimization capabilities.