scholarly journals A New Adaptive Hungarian Mating Scheme in Genetic Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-13
Author(s):  
Chanju Jung ◽  
Yong-Hyuk Kim ◽  
Yourim Yoon ◽  
Byung-Ro Moon
Keyword(s):  
Genetic Algorithms ◽  
Combinatorial Optimization ◽  
Optimization Problems ◽  
Adaptive Strategy ◽  
Test Bed ◽  
Random Matching ◽  
Hybrid Schemes ◽  
Combinatorial Optimization Problems ◽  
Graph Bisection ◽  
Mating Scheme

In genetic algorithms, selection or mating scheme is one of the important operations. In this paper, we suggest an adaptive mating scheme using previously suggested Hungarian mating schemes. Hungarian mating schemes consist of maximizing the sum of mating distances, minimizing the sum, and random matching. We propose an algorithm to elect one of these Hungarian mating schemes. Every mated pair of solutions has to vote for the next generation mating scheme. The distance between parents and the distance between parent and offspring are considered when they vote. Well-known combinatorial optimization problems, the traveling salesperson problem, and the graph bisection problem are used for the test bed of our method. Our adaptive strategy showed better results than not only pure and previous hybrid schemes but also existing distance-based mating schemes.

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.

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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