ABRASHI, Arijan ;ŠTEFANIĆ, Nedjeljko ;LISJAK, Dragutin . Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 56, n.5, p. 330-339, october 2017. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/solving-jssp-by-introducing-hamilton-similarity-and-time-dependent-fitness-scaling/>. Date accessed: 19 nov. 2024. doi:http://dx.doi.org/.
Abrashi, A., Štefanić, N., & Lisjak, D. (2010). Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling. Strojniški vestnik - Journal of Mechanical Engineering, 56(5), 330-339. doi:http://dx.doi.org/
@article{.,
author = {Arijan Abrashi and Nedjeljko Štefanić and Dragutin Lisjak},
title = {Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {56},
number = {5},
year = {2010},
keywords = {genetic algorithm; Hamilton similarity; niching; time dependent fitness scaling; Job shop scheduling problem; },
abstract = {In this paper we propose and test a niching genetic algorithm (GA), which uses the so-called Hamilton similarity for a comparison of individuals in the population. The advantage of the Hamilton similarity lies in the fact that there is no need for context sensitive information in order to successfully compare two population members. Furthermore, the algorithm was tested on the famous Job Shop Scheduling Problem (JSSP) - benchmark mt10, and statistical results of the test were given. Significantly smaller standard deviation of the proposed GA compared to Simple GA clearly demonstrates its superiority. In addition to the Hamilton similarity, time dependent fitness scaling was proposed which in conjunction with niching significantly reduces the probability of the algorithm to get stuck in one of the less desirable local optimum. Finally, suggestions for future research are given.},
issn = {0039-2480}, pages = {330-339}, doi = {},
url = {https://www.sv-jme.eu/article/solving-jssp-by-introducing-hamilton-similarity-and-time-dependent-fitness-scaling/}
}
Abrashi, A.,Štefanić, N.,Lisjak, D. 2010 October 56. Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 56:5
%A Abrashi, Arijan %A Štefanić, Nedjeljko %A Lisjak, Dragutin %D 2010 %T Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling %B 2010 %9 genetic algorithm; Hamilton similarity; niching; time dependent fitness scaling; Job shop scheduling problem; %! Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling %K genetic algorithm; Hamilton similarity; niching; time dependent fitness scaling; Job shop scheduling problem; %X In this paper we propose and test a niching genetic algorithm (GA), which uses the so-called Hamilton similarity for a comparison of individuals in the population. The advantage of the Hamilton similarity lies in the fact that there is no need for context sensitive information in order to successfully compare two population members. Furthermore, the algorithm was tested on the famous Job Shop Scheduling Problem (JSSP) - benchmark mt10, and statistical results of the test were given. Significantly smaller standard deviation of the proposed GA compared to Simple GA clearly demonstrates its superiority. In addition to the Hamilton similarity, time dependent fitness scaling was proposed which in conjunction with niching significantly reduces the probability of the algorithm to get stuck in one of the less desirable local optimum. Finally, suggestions for future research are given. %U https://www.sv-jme.eu/article/solving-jssp-by-introducing-hamilton-similarity-and-time-dependent-fitness-scaling/ %0 Journal Article %R %& 330 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 56 %N 5 %@ 0039-2480 %8 2017-10-24 %7 2017-10-24
Abrashi, Arijan, Nedjeljko Štefanić, & Dragutin Lisjak. "Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling." Strojniški vestnik - Journal of Mechanical Engineering [Online], 56.5 (2010): 330-339. Web. 19 Nov. 2024
TY - JOUR AU - Abrashi, Arijan AU - Štefanić, Nedjeljko AU - Lisjak, Dragutin PY - 2010 TI - Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling JF - Strojniški vestnik - Journal of Mechanical Engineering DO - KW - genetic algorithm; Hamilton similarity; niching; time dependent fitness scaling; Job shop scheduling problem; N2 - In this paper we propose and test a niching genetic algorithm (GA), which uses the so-called Hamilton similarity for a comparison of individuals in the population. The advantage of the Hamilton similarity lies in the fact that there is no need for context sensitive information in order to successfully compare two population members. Furthermore, the algorithm was tested on the famous Job Shop Scheduling Problem (JSSP) - benchmark mt10, and statistical results of the test were given. Significantly smaller standard deviation of the proposed GA compared to Simple GA clearly demonstrates its superiority. In addition to the Hamilton similarity, time dependent fitness scaling was proposed which in conjunction with niching significantly reduces the probability of the algorithm to get stuck in one of the less desirable local optimum. Finally, suggestions for future research are given. UR - https://www.sv-jme.eu/article/solving-jssp-by-introducing-hamilton-similarity-and-time-dependent-fitness-scaling/
@article{{}{.},
author = {Abrashi, A., Štefanić, N., Lisjak, D.},
title = {Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {56},
number = {5},
year = {2010},
doi = {},
url = {https://www.sv-jme.eu/article/solving-jssp-by-introducing-hamilton-similarity-and-time-dependent-fitness-scaling/}
}
TY - JOUR AU - Abrashi, Arijan AU - Štefanić, Nedjeljko AU - Lisjak, Dragutin PY - 2017/10/24 TI - Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 56, No 5 (2010): Strojniški vestnik - Journal of Mechanical Engineering DO - KW - genetic algorithm, Hamilton similarity, niching, time dependent fitness scaling, Job shop scheduling problem, N2 - In this paper we propose and test a niching genetic algorithm (GA), which uses the so-called Hamilton similarity for a comparison of individuals in the population. The advantage of the Hamilton similarity lies in the fact that there is no need for context sensitive information in order to successfully compare two population members. Furthermore, the algorithm was tested on the famous Job Shop Scheduling Problem (JSSP) - benchmark mt10, and statistical results of the test were given. Significantly smaller standard deviation of the proposed GA compared to Simple GA clearly demonstrates its superiority. In addition to the Hamilton similarity, time dependent fitness scaling was proposed which in conjunction with niching significantly reduces the probability of the algorithm to get stuck in one of the less desirable local optimum. Finally, suggestions for future research are given. UR - https://www.sv-jme.eu/article/solving-jssp-by-introducing-hamilton-similarity-and-time-dependent-fitness-scaling/
Abrashi, Arijan, Štefanić, Nedjeljko, AND Lisjak, Dragutin. "Solving JSSP by Introducing Hamilton Similarity and Time Dependent Fitness Scaling" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 56 Number 5 (24 October 2017)
Strojniški vestnik - Journal of Mechanical Engineering 56(2010)5, 330-339
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
In this paper we propose and test a niching genetic algorithm (GA), which uses the so-called Hamilton similarity for a comparison of individuals in the population. The advantage of the Hamilton similarity lies in the fact that there is no need for context sensitive information in order to successfully compare two population members. Furthermore, the algorithm was tested on the famous Job Shop Scheduling Problem (JSSP) - benchmark mt10, and statistical results of the test were given. Significantly smaller standard deviation of the proposed GA compared to Simple GA clearly demonstrates its superiority. In addition to the Hamilton similarity, time dependent fitness scaling was proposed which in conjunction with niching significantly reduces the probability of the algorithm to get stuck in one of the less desirable local optimum. Finally, suggestions for future research are given.