Authors:
G. A. Papakostas
1
;
Y. S. Boutalis
1
;
D. A. Karras
2
and
B. G. Mertzios
3
Affiliations:
1
Democritus University of Thrace, Greece
;
2
Chalkis Institute of Technology; Hellenic Open University, Greece
;
3
Thessaloniki Institute of Technolog, Greece
Keyword(s):
Genetic Algorithms, Diversity, Clustering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computation and Control
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Optimization Algorithms
;
Soft Computing
Abstract:
In this paper, a novel measure of the population diversity of a Genetic Algorithm (GA) is presented. Chromosomes diversity plays a major role for the successfully operation of a GA, since it describes the number of the different candidate solutions that the algorithm evaluates, in order to find the optimal one, in respect to a performance index, called objective function. In a well defined algorithm, the diversity of the current population should be measurable, in order to estimate the performance of the algorithm. The resulted observation, that is, the measuring of the diversity, can then be used to real-time adjust the factors that determine the chromosomes variety (Pc, Pm), during the execution of the GA. It is shown, that a simple chromosomes clustering into the search space, by using the well known k-means algorithm, can give a useful picture of the population’s distribution. Thus, by translating the problem of finding the best solution to a GA-based problem into an iterative cl
ustering process, and by using the scatter matrices (Sw, Sb), which describe completely the candidate’s solutions topology, one could define a novel formula that gives the population diversity of the algorithm.
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