Authors:
Saeed Mehrabi
1
;
Abbas Mehrabi
2
and
Ali D. Mehrabi
3
Affiliations:
1
Shahid Bahonar University of Kerman, Iran, Islamic Republic of
;
2
Islamic Azad University, Iran, Islamic Republic of
;
3
Yazd University, Iran, Islamic Republic of
Keyword(s):
Algorithmic Graph Theory, Combinatorial Optimization, Maximum Independent Set Problem, Genetic Algorithms.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Enterprise Software Technologies
;
Intelligent Problem Solving
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Software Engineering
;
Symbolic Systems
Abstract:
In recent years, Genetic Algorithms (GAs) have been frequently used for many search and optimization problems. In this paper, we use genetic algorithms for solving the NP-complete maximum independent set problem (MISP). We have developed a new heuristic independent crossover (HIX) especially for MISP, introducing a new hybrid genetic algorithm (MIS-HGA). We use a repair operator to ensure that our offsprings are valid after mutation. We compare our algorithm, MIS-GA, with an efficient existing algorithm called GENEsYs. Also, a variety of benchmarks are used to test our algorithm. As the experimental results show: 1) our algorithm outperforms GENEsYs, and, 2) applying HIX to genetic algorithms with an appropriate mutation rate, gives far better performance than the classical crossover operators.