Authors:
M. Gestal
1
;
D. Rivero
1
;
E. Fernández
1
;
J. R. Rabuñal
2
and
J. Dorado
2
Affiliations:
1
University of A Coruña, Spain
;
2
University of Coruña, Spain
Keyword(s):
Evolutionary Computation, Diversity, Genetic Drift.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Enterprise Information Systems
;
Evolutionary Computing
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Industrial Applications of AI
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Genetic Algorithms (GAs) are a technique that has given good results to those problems that require a search through a complex space of possible solutions. A key point of GAs is the necessity of maintaining the diversity in the population. Without this diversity, the population converges and the search prematurely stops, not being able to reach the optimal solution. This is a very common situation in GAs. This paper proposes a modification in traditional GAs to overcome this problem, avoiding the loose of diversity in the population. This modification allows an exhaustive search that will provide more than one valid solution in the same execution of the algorithm.