Designing a Hybrid Genetic Algorithm for the Linear Ordering Problem

Abstract

Abstract. The Linear Ordering Problem(LOP), which is a well-known N P-hard problem, has numerous applications in various fields. Using this problem as an example, we illustrate a general procedure of designing a hybrid genetic algorithm, which includes the selection of crossover/mutation operators, accelerating the local search module and tuning the parameters. Experimental results show that our hybrid genetic algorithm outperforms all other existing exact and heuristic algorithms for this problem.

A survey on the linear ordering problem for weighted or unweighted tournaments

“…We give below some indications on how to adapt some metaheuristics to the linear ordering problem. Other applications can be found in Belloni and Lucena (2004), Campos et al (1999Campos et al ( , 2001, Chaovalitwongse and Pardalos (1997), Hudry (1998), Congram (2000), Gamboa et al (2006), Garcia et al (2006), González andPérez-Brito (2001), Huang and Lim (2003), Hudry (1989), Laguna et al (1999), Stützle (2003, 2004).…”

Implementing Artificial Immune Systems for the Linear Ordering Problem

“…A number of heuristics and soft computing algorithms was used for solving LOP instances: greedy algorithms, local search algorithms, elite tabu search, scattered search and iterated local search [21,9,15].The metaheuristic algorithms used to solve LOP in the past include genetic algorithms [12,13], differential evolution [23], and ant colony based algorithms [4]. The investigation of metaheuristics and soft computing algorithms is motivated by previous success of such methods in real world and industrial applications [1,5,29,22] 3 Artificial Immune Systems…”

A benchmark library and a comparison of heuristic methods for the linear ordering problem

“…In [19] a similar method based on combining a classical GA with a local search is presented. It is called hybrid genetic algorithm (HGA) and it is very similar to the method in [31].…”