TY - GEN
T1 - On the influence of using initialization functions on genetic algorithms solving combinatorial optimization problems
T2 - 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2014
AU - Osaba, E.
AU - Carballedo, R.
AU - Diaz, F.
AU - Onieva, E.
AU - Lopez, P.
AU - Perallos, A.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014
Y1 - 2014
N2 - Combinatorial optimization is a widely studied field within artificial intelligence. There are many problems of this type, and many techniques applied to them can be found in the literature. Especially, population techniques have received much attention in this area, being genetic algorithms (GA) the most famous ones. Although throughout history many studies on GAs have been performed, there is still no study like the presented in this work. In this paper, a study on the influence of using heuristic initialization functions in genetic algorithms (GA) applied to combinatorial optimization problems is performed. Being the first phase of this research, the study is conducted using one of the best known problems in combinatorial optimization: the traveling salesman problem. Three different experimentations are carried out, using three different heuristic initialization functions. Additionally, for each experiment four versions of a GA have been developed for the comparison. Each of these variant differs in the initialization phase. The results obtained by each GA are compared to determine the influence of the use of heuristic functions for the initialization of the population.
AB - Combinatorial optimization is a widely studied field within artificial intelligence. There are many problems of this type, and many techniques applied to them can be found in the literature. Especially, population techniques have received much attention in this area, being genetic algorithms (GA) the most famous ones. Although throughout history many studies on GAs have been performed, there is still no study like the presented in this work. In this paper, a study on the influence of using heuristic initialization functions in genetic algorithms (GA) applied to combinatorial optimization problems is performed. Being the first phase of this research, the study is conducted using one of the best known problems in combinatorial optimization: the traveling salesman problem. Three different experimentations are carried out, using three different heuristic initialization functions. Additionally, for each experiment four versions of a GA have been developed for the comparison. Each of these variant differs in the initialization phase. The results obtained by each GA are compared to determine the influence of the use of heuristic functions for the initialization of the population.
KW - Combinatorial optimization
KW - Genetic algorithm
KW - Initialization
KW - Meta-heuristic
KW - Traveling Salesman problem
UR - https://www.scopus.com/pages/publications/84920532174
U2 - 10.1109/eais.2014.6867465
DO - 10.1109/eais.2014.6867465
M3 - Conference contribution
AN - SCOPUS:84920532174
T3 - 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2014 - Conference Proceedings
BT - 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2014 - Conference Proceedings
A2 - Angelov, Plamen
A2 - Filev, Dimitar
A2 - Kasabov, Nikola
A2 - Lughofer, Edwin
A2 - Klement, Erich Peter
A2 - Saminger-Platz, Susanne
A2 - Iglesias, Jose A.
A2 - Sayed-Mouchaweh, Moamar
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 2 June 2014 through 4 June 2014
ER -