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This article addresses the dynamic vehicle routing problem (DVRP). DVRP is a challenging variation of the classic vehicle routing problem in which some customers are not known in advance. The objective is to incorporate new customers into the schedule as they become known while still attempting to minimize the cost of serving all customers without violating the problem constraints. This work proposes an effective population-based approach that integrates various algorithmic components to address DVRP. The approach combines a local search algorithm with various evolutionary operators (crossover and mutation) in an adaptive manner. To promote diversity, the proposed approach utilizes a population of solutions and uses a quality-and-diversity strategy to retain only promising solutions. The well-known 21 DVRP benchmark instances are utilized to test the performance of the proposed approach. An …