Genetic Algorithm Optimization Program
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This article explores optimization techniques for simple univariate functions. The optimization process involves identifying maximum/minimum values and determining extremum locations. We delve into various methodologies, including traditional analytical approaches and modern numerical optimization algorithms. The discussion covers method selection criteria and parameter tuning strategies for improved algorithmic performance. Through practical code examples, we demonstrate implementation approaches using genetic algorithms - featuring population initialization, fitness evaluation, selection operators (tournament/roulette wheel), crossover mechanisms (single-point/two-point), and mutation operations. Key MATLAB functions like ga() and optimization toolbox components will be explained. Readers will learn to apply univariate optimization to real-world problems while enhancing mathematical proficiency through in-depth analysis of optimization challenges.
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