Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals

2018 ◽  
pp. 424-431
Author(s):  
Michael Affenzeller ◽  
Bogdan Burlacu ◽  
Stephan Winkler ◽  
Michael Kommenda ◽  
Gabriel Kronberger ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Early Stopping ◽  
Stopping Criteria

scholarly journals Early stopping turbo iteration at low SNR for CE-based stopping criteria

2014 ◽  
Author(s):  
Roslina Mohamad ◽  
Harlisya Harun ◽  
Makhfudzah Mokhtar ◽  
W. A. W Adnan ◽  
N. M. Anas ◽  
...  
Keyword(s):  
Early Stopping ◽  
Stopping Criteria ◽  
Low Snr

scholarly journals An Assembly Line Balancing Problem Automotive Cables

2015 ◽  
Vol 6 (1) ◽  
pp. 59-66
Author(s):  
Hager Triki ◽  
Wafik Hachicha ◽  
Ahmed Mellouli ◽  
Faouzi Masmoudi
Keyword(s):  
Genetic Algorithm ◽  
Experimental Study ◽  
Assembly Line ◽  
Assembly Line Balancing ◽  
Line Balancing ◽  
Computational Time ◽  
Stopping Criteria ◽  
Reasonable Time ◽  
Assembly Line Balancing Problem ◽  
Full Factorial

Abstract In this paper, an Assembly Line Balancing Problem (ALBP) is presented in a real-world automotive cables manufacturer company. This company found it necessary to balance its line, since it needs to increase the production rate. In this ALBP, the number of stations is known and the objective is to minimize cycle time where both precedence and zoning constrains must be satisfied. This problem is formulated as a binary linear program (BLP). Since this problem is NP-hard, an innovative Genetic Algorithm (GA) is implemented. The full factorial design is used to obtain the better combination GA parameters and a simple convergence experimental study is performed on the stopping criteria to reduce computational time. Comparison of the proposed GA results with CPLEX software shows that, in a reasonable time, the GA generates consistent solutions that are very close to their optimal ones. Therefore, the proposed GA approach is very effective and competitive.

scholarly journals The saturation of population fitness as a stopping criterion in genetic algorithm

2019 ◽  
Vol 9 (5) ◽  
pp. 4130
Author(s):  
Foo Fong Yeng ◽  
Soo Kum Yoke ◽  
Azrina Suhaimi
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problems ◽  
Global Optimum ◽  
Local Optimum ◽  
Stopping Criterion ◽  
Genetic Operators ◽  
Stopping Criteria ◽  
Optimum Solution ◽  
Global Optimum Solution ◽  
Population Fitness

Genetic Algorithm is an algorithm imitating the natural evolution process in solving optimization problems. All feasible (candidate) solutions would be encoded into chromosomes and undergo the execution of genetic operators in evolution. The evolution itself is a process searching for optimum solution. The searching would stop when a stopping criterion is met. Then, the fittest chromosome of last generation is declared as the optimum solution. However, this optimum solution might be a local optimum or a global optimum solution. Hence, an appropriate stopping criterion is important such that the search is not ended before a global optimum solution is found. In this paper, saturation of population fitness is proposed as a stopping criterion for ending the search. The proposed stopping criteria was compared with conventional stopping criterion, fittest chromosomes repetition, under various parameters setting. The results show that the performance of proposed stopping criterion is superior as compared to the conventional stopping criterion.

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