Dimensionality Reduction Code for Modeling Independent Variables Using Genetic Algorithm

In this paper, we will conduct an in-depth exploration of optimization computation based on the genetic algorithm (GA) methodology. Genetic algorithms represent a class of evolutionary computation techniques inspired by fundamental principles of natural selection and genetics. This approach mimics biological evolutionary processes to search for optimal solutions by simulating natural evolution mechanisms through population-based operations. GAs have been widely adopted across various domains including engineering design, production scheduling, and financial analysis. Our discussion will cover the fundamental principles and optimization procedures of genetic algorithms, along with their practical applications in different fields. We will particularly focus on implementation strategies for applying GAs in optimization computations, including key algorithmic components such as population initialization using random sampling or heuristic methods, fitness evaluation functions, and genetic operators implementation (selection via roulette wheel or tournament methods, crossover using single-point or multi-point techniques, and mutation operations). The paper also provides comparative analysis between genetic algorithms and other optimization approaches, highlighting their respective advantages in handling complex, non-linear problems. Through this study, readers will gain comprehensive understanding of GA fundamentals and practical applications, enabling them to effectively implement genetic algorithms with proper parameter tuning (population size, crossover/mutation rates) for their specific optimization challenges to achieve enhanced results.