Some Further Experiments with Crossover Operators for Genetic Algorithms

Misevičius, Alfonsas; Kuznecovaitė, Dovilė; Platuziene, Jurate

doi:10.15388/informatica.2018.178

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…Several crossover operators were implemented and examined. Short descriptions of the crossover procedures are provided below (see also [ 124 , 125 ]).…”

Section: Hybrid Genetic-hierarchical Algorithm For the Quadratic Amentioning

confidence: 99%

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A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

Misevičius

Verenė

2021

Entropy

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In this paper, we present a hybrid genetic-hierarchical algorithm for the solution of the quadratic assignment problem. The main distinguishing aspect of the proposed algorithm is that this is an innovative hybrid genetic algorithm with the original, hierarchical architecture. In particular, the genetic algorithm is combined with the so-called hierarchical (self-similar) iterated tabu search algorithm, which serves as a powerful local optimizer (local improvement algorithm) of the offspring solutions produced by the crossover operator of the genetic algorithm. The results of the conducted computational experiments demonstrate the promising performance and competitiveness of the proposed algorithm.

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“…Several crossover operators were implemented and examined. Short descriptions of the crossover procedures are provided below (see also [ 124 , 125 ]).…”

Section: Hybrid Genetic-hierarchical Algorithm For the Quadratic Amentioning

confidence: 99%

“…The universal crossover (UNIVX) [ 124 ] distinguishes for its versatility and the possibility of flexible usage depending on the specific needs of the user. It is somewhat similar to what is known as a simulated binary crossover [ 126 ].…”

Section: Hybrid Genetic-hierarchical Algorithm For the Quadratic Amentioning

confidence: 99%

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

Misevičius

Verenė

2021

Entropy

Self Cite

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“…The most frequently used algorithms in the literature are genetic algorithm, simulated annealing, variable neighbourhood search, tabu search, etc. (see Gomes et al, 2014;Reisi-Nafchi and Moslehi, 2015;Kurdi, 2015;Zhang and Wong, 2016;Martin et al, 2016;Akbari and Rashidi, 2016;Niroomand et al, 2016;Quintana et al, 2017;Hsieh, 2017;Hu et al, 2016;Ghadiri Nejad and Banar, 2018;Misevičius et al, 2018;Vizvári et al, 2018;Dugonik et al, 2019;Ullah et al, 2020;Hassanpour, 2020;Aliya et al, 2020;Fernández et al, 2020;Hussain and Khan, 2020).…”

Section: Introductionmentioning

confidence: 99%

Tabu Search Based Hybrid Meta-Heuristic Approaches for Schedule-Based Production Cost Minimization Problem for the Case of Cable Manufacturing Systems

Daneshdoost¹,

Hajiaghaei–Keshteli²,

Şahin³

et al. 2022

Informatica

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This paper models and solves the scheduling problem of cable manufacturing industries that minimizes the total production cost, including processing, setup, and storing costs. Two hybrid meta-heuristics, which combine simulated annealing and variable neighbourhood search algorithms with tabu search algorithm, are proposed. Applying some case-based theorems and rules, a special initial solution with optimal setup cost is obtained for the algorithms. The computational experiments, including parameter tuning and final experiments over the benchmarks obtained from a real cable manufacturing factory, show superiority of the combination of tabu search and simulated annealing comparing to the other proposed hybrid and classical meta-heuristics.

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“…It is a method to find the optimal solution or approximate optimal solution to a complex problem by simulating the natural evolution process. It has been used in neural networks 22 , combinatorial optimization 23 , artificial intelligence 24 , 25 , genetic programming 26 , data mining 27 and other fields. The optimization principle is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

Qiu

Sun

et al. 2022

Sci Rep

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Since the outbreak of the 2019 Coronavirus disease (COVID-19) at the end of 2019, it has caused great adverse effects on the whole world, and it has been hindering the global economy. It is ergent to establish an infectious disease model for the current COVID-19 epidemic to predict the trend of the epidemic. Based on the SEIR model, the improved SEIR models were established with considering the incubation period, the isolated population, and genetic algorithm (GA) parameter optimization method. The improved SEIR models can predict the trend of the epidemic situation better and obtain the more accurate epidemic-related parameters. Comparing some key parameters, it is capable to evaluate the impact of different epidemic prevention measures and the implementation of different epidemic prevention levels on the COVID-19, which has significant guidance for further epidemic prevention measures.

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Some Further Experiments with Crossover Operators for Genetic Algorithms

Cited by 6 publications

References 16 publications

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

Tabu Search Based Hybrid Meta-Heuristic Approaches for Schedule-Based Production Cost Minimization Problem for the Case of Cable Manufacturing Systems

Application of genetic algorithm combined with improved SEIR model in predicting the epidemic trend of COVID-19, China

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