Gendreau et al.  described three approaches to the VRP with time windows, where each approach ensures feasibility differently. The first approach ensures feasibility while sacrificing the evolutionary aspects of the genetic algorithm; thus, it is considered a hybrid approach. This approach does not allow infeasible reproduction or mutation. The second approach applies a genetic algorithm to the partitioning of customers to routes, but the routes are solved separately to ensure feasibility. The third approach applies a genetic algorithm directly, but incorporates a post-processing step to ensure feasibility. The third approach yielded the best results in terms of the objective; however, it was computationally expensive. Gendreau et al.  stated that accounting for the constraints of the VRP makes a genetic algorithm computationally expensive.
In computing terms, genetic algorithms map strings of numbers to each potential solution. Each solution becomes an individual in the population, and each string becomes a representation of an individual. There should be a way to derive each individual from its string representation. The genetic algorithm then manipulates the most promising strings in its search for an improved solution. The algorithm operates through a simple cycle:
Genetic Algorithm Phd Thesis | write my paper for me
A genetic algorithm is a global search procedure that solves problems by emulating evolution. A pure genetic algorithm uses reproduction and mutation to develop a new generation of solutions from the current generation of solutions. The constraints of the SDVRP do not allow the application of pure genetic algorithms without an additional step to ensure feasibility. A hybrid genetic algorithm allows for a genetic global search procedure while ensuring feasibility. The phrase hybrid genetic algorithm is sometimes used to describe memetic algorithms; however, for this paper hybrid refers to composing a solution from multiple sources. Coupling hybrid and genetic algorithms yields the term hybrid genetic algorithm. This section is organized as follows, Section 3.1 describes the development of an initial population and the reproduction procedure is discussed in Section 3.2.