Genetic Algorithm Implementation for Knapsack Problem
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In this article, I will share MATLAB code that implements a genetic algorithm to solve the knapsack problem. The knapsack problem represents a classic combinatorial optimization challenge with wide-ranging applications in practical scenarios. Through genetic algorithm implementation, we can identify optimal solutions that satisfy capacity constraints while maximizing total value. The algorithm employs key components including population initialization, fitness evaluation using value-to-weight ratios, tournament selection for parent chromosomes, uniform crossover for offspring generation, and mutation operators to maintain diversity. This implementation features adaptive parameter tuning and elitism preservation to enhance convergence efficiency. I will provide detailed explanations of the algorithmic workflow and share complete code references for study and adaptation purposes.
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