Royal Lineage Genetic Algorithm for a Better Solution to Traveling Salesman Problem

Genetic Algorithms (GAs) are famous for solving real-world problems despite a tendency to converge prematurely. This paper introduces the Royal Lineage Genetic Algorithm (RoLiGA), a GA enhancement inspired by chromosomal inheritance in royal lineages. The RoLiGA employed several algorithms, including the insertion heuristic, elitist domination procedure, fitness-based-roulette selection, multi-prince family's regeneration, partially mapped crossover, repetitive 2-point swap mutation, and 2-opt heuristics. The combination ensures the chromosomal inheritance process leads to optimal solutions. Tested on 26 Traveling Salesman Problem (TSP) datasets, RoLiGA outperforms five GA-based hybrid algorithms, obtaining the best-known values and setting new benchmarks for solution quality.