Application Example of Multi-Objective Optimization Using Genetic Algorithms
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In this article, we will demonstrate the application of genetic algorithms in multi-objective optimization, particularly in maximizing nonlinear functions. We will detail the fundamental principles and step-by-step procedures of genetic algorithms, illustrating their effectiveness through concrete case studies. The implementation typically involves key components such as population initialization, fitness evaluation using objective functions, selection operations (e.g., tournament selection), crossover with techniques like simulated binary crossover (SBX), and mutation operators (e.g., polynomial mutation). By employing genetic algorithms, we can effectively optimize nonlinear functions to achieve higher maximum values through iterative evolution of candidate solutions. This approach finds broad applications in real-world problems and proves highly efficient when addressing complex optimization challenges involving multiple conflicting objectives.
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