  The "0-1 knapsack problem" stands as a classical combinatorial optimization
conundrum, necessitating the selection of a subset of items from a given set.
Each item possesses inherent values and weights, and the primary objective is
to formulate a selection strategy that maximizes the total value while adhering
to a predefined capacity constraint. In this research paper, we introduce a
novel variant of Cultural Algorithms tailored specifically for solving 0-1
knapsack problems, a well-known combinatorial optimization challenge. Our
proposed algorithm incorporates a belief space to refine the population and
introduces two vital functions for dynamically adjusting the crossover and
mutation rates during the evolutionary process. Through extensive
experimentation, we provide compelling evidence of the algorithm's remarkable
efficiency in consistently locating the global optimum, even in knapsack
problems characterized by high dimensions and intricate constraints.
