Genetic Algorithm (Decimal Encoding)

Genetic Algorithm (Decimal Encoding) is an optimization algorithm that simulates the evolutionary processes found in nature. This algorithm progressively optimizes solutions by mimicking genetic operations such as selection, crossover, and mutation. In decimal-encoded genetic algorithms, potential solutions are represented as strings of decimal-encoded genes, where each gene corresponds to a parameter value in the solution space. The algorithm operates through three primary genetic operators: selection (typically implemented using roulette wheel or tournament selection methods), crossover (achieved through arithmetic crossover or simulated binary crossover operators), and mutation (using Gaussian mutation or uniform mutation techniques). These operations allow the algorithm to continuously improve solution quality while maintaining population diversity. Key implementation aspects include fitness function evaluation, population initialization with decimal-encoded chromosomes, and termination criteria checking. The algorithm maintains a population of candidate solutions and evolves them over generations through fitness-based selection and genetic operations. Genetic algorithms with decimal encoding are particularly effective for continuous optimization problems and find extensive applications in engineering optimization, machine learning model parameter tuning, data mining feature selection, and complex system design. By employing decimal-encoded genetic algorithms, researchers can discover high-quality solutions for complex problems and efficiently explore large solution spaces that may be challenging for traditional optimization methods.