scholarly journals Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-22 ◽  
Author(s):  
E. Osaba ◽  
R. Carballedo ◽  
F. Diaz ◽  
E. Onieva ◽  
I. de la Iglesia ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Comparative Analysis ◽  
Combinatorial Optimization ◽  
Scientific Community ◽  
Statistical Study ◽  
Optimization Problems ◽  
Basic Structure ◽  
Evolutionary Strategy ◽  
Mutation Process ◽  
Combinatorial Optimization Problems

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distributionz-test.

scholarly journals Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
E. Osaba ◽  
F. Diaz ◽  
R. Carballedo ◽  
E. Onieva ◽  
A. Perallos
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Traveling Salesman ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Golden Ball ◽  
The One

Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results.

scholarly journals Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

2021 ◽  
Vol 11 (14) ◽  
pp. 6449
Author(s):  
Fernando Peres ◽  
Mauro Castelli
Keyword(s):  
Combinatorial Optimization ◽  
Scientific Community ◽  
Optimization Problems ◽  
Research Field ◽  
Machine Learning Algorithms ◽  
Combinatorial Optimization Problems ◽  
The Past ◽  
Definition Of ◽  
Problem Solvers ◽  
New Algorithms

In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

A GA-BASED APPROACH TO OPTIMIZING COMBINATIONAL QUERIES IN SCDL

2008 ◽  
Vol 02 (02) ◽  
pp. 273-289 ◽  
Author(s):  
WEIQIN YING ◽  
YUANXIANG LI ◽  
PHILLIP C.-Y. SHEU
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Description Language ◽  
Combinatorial Optimization Problems ◽  
One To One

The semantic capability description language (SCDL) allows users to describe combinatorial optimization problems declaratively. We employ genetic algorithms (GA) and penalty techniques to process unconstrained SCDL queries and singly constrained SCDL queries, and determine a "one-to-one" mapping between queries and algorithms. Based on such, we develop a GA-based "one-to-many" mapping to process and integrate multi-constrained SCDL queries.

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