Genotype vs. Phenotype: A Crossover Operator Comparison for the Multi-Objective Coverage Path Planning Problem

The crossover operator is a fundamental component of genetic algorithms, combining genetic material from parent solutions to generate offspring. Traditionally, crossover is performed in the search space using the genotype. However, it can also be executed in the solution space on the phenotype, offering potential advantages such as improved feasibility preservation, faster convergence, and greater explainability. These benefits, however, come with tradeoffs, including increased implementation complexity, higher computational costs, and a likely reduction in solution diversity. This study examines the properties of search space and solution space crossover operators in the context of a multi-objective, weighted, and continuous coverage path planning problem. Three crossover strategies are tested: two of which operate directly on the genotype and one that uses intersections of the phenotype.