Genetic algorithm with a new round-robin based tournament selection: Statistical properties analysis

Abstract

A round-robin tournament is a contest where each and every player plays with all the other players. In this study, we propose a round-robin based tournament selection operator for the genetic algorithms (GAs). At first, we divide the whole population into two equal and disjoint groups, then each individual of a group competes with all the individuals of other group. Statistical experimental results reveal that the devised selection operator has a relatively better selection pressure along with a minimal loss of population diversity. For the consisting of assigned probability distribution with sampling algorithms, we employ the Pearson’s chi-square and the empirical distribution function as goodness of fit tests for the analysis of statistical properties analysis. At the cost of a nominal increase of the complexity as compared to conventional selection approaches, it has improved the sampling accuracy. Finally, for the global performance, we considered the traveling salesman problem to measure the efficiency of the newly developed selection scheme with respect to other competing selection operators and observed an improved performance.

An efficient genetic algorithm for solving the travel salesman problem

“…This selection technique has a wide range of applications; see, Schell and Wegenkittl (2001); Lee et al (2008) and Hussain et al (2022).…”

“…A recently introduced alternative selection method is the round-robin based tournament selection (RRTS), by Hussain et al (2022). Their research provides evidence supporting the effectiveness of this approach in maintaining a delicate balance between exploration and exploitation.…”

Leveraging Multi-Criteria Integer Programming Optimization for Effective Team Formation

“…Upon convergence, the algorithm forms 𝐾 clusters of students, where each student is assigned to the cluster that minimizes the diversity level of their attributes. Upon convergence, round-robin sampling [67] creates balanced 𝑁 𝓟 sub-populations {𝓟 𝑘 } 𝑘=1 𝑁 𝓟 with roughly equal sizes of 𝑁 𝑠𝑢𝑏 . The resulting balanced subpopulations are optimal for the next stage of team formation optimization.…”