An improved genetic algorithm for numerical function optimization

Song, Yingying; Wang, Fulin; Chen, Xinxin

doi:10.1007/s10489-018-1370-4

Cited by 83 publications

(62 citation statements)

References 37 publications

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“…Similarly, the offspring individual Y j ( j = n /2 + 1, n /2 + 2,…, n ) generated by crossover according to equation (7) is near the optimal individual X 1 ′ in the population. Because X 1 ′ is the best individual in the population, the offsprings generated by crossover with the HNDDBX operator are expected to be better than those of the crossover operator in [31, 37].…”

Section: Multi-offspring Improved Real-coded Genetic Algorithm (Momentioning

confidence: 99%

“…At this point, there are countless search directions, and Y k may be located at any point within X 1 ′ CFG . In addition, because X 1 ′ is better than X i ′, the offsprings Y k generated by the HNDDBX operator have a great possibility to be superior to those of the crossover operator in [31, 37]. Thus, Y k may be very close to the optimal solution X ∗ of the problem to be solved.…”

Section: Multi-offspring Improved Real-coded Genetic Algorithm (Momentioning

confidence: 99%

“…In summary, the offsprings generated by the HNDDBX crossover operator are better than those of the crossover operator in [31, 37]. Therefore, the HNDDBX operator can significantly improve the convergence speed of MOIRCGA.…”

Section: Multi-offspring Improved Real-coded Genetic Algorithm (Momentioning

confidence: 99%

“…During the last few decades, the RCGA has attracted a lot of attention because of its unique and excellent performance. Many scholars have made in-depth research on RCGA and achieved excellent research results [23–31]. Because crossover operators and mutation operators have a great influence on the performance of GA, many scholars have focused their attention on the improvement of crossover operators and mutation operators.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

Wang

Zhang

Ersoy

et al. 2019

Computational Intelligence and Neuroscience

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A multi-offspring improved real-coded genetic algorithm (MOIRCGA) using the heuristical normal distribution and direction-based crossover (HNDDBX) is proposed to solve constrained optimization problems. Firstly, a HNDDBX operator is proposed. It guarantees the cross-generated offsprings are located near the better individuals in the population. In this way, the HNDDBX operator ensures that there is a great chance of generating better offsprings. Secondly, as iterations increase, the same individuals are likely to appear in the population. Therefore, it is possible that the two parents of participation crossover are the same. Under these circumstances, the crossover operation does not generate new individuals, and therefore does not work. To avoid this problem, the substitution operation is added after the crossover so that there is no duplication of the same individuals in the population. This improves the computational efficiency of MOIRCGA by leading it to quickly converge to the global optimal solution. Finally, aiming at the shortcoming of a single mutation operator which cannot simultaneously take into account local search and global search, a Combinational Mutation method is proposed with both local search and global search. The experimental results with sixteen examples show that the multi-offspring improved real-coded genetic algorithm (MOIRCGA) has fast convergence speed. As an example, the optimization model of the cantilevered beam structure is formulated, and the proposed MOIRCGA is compared to the RCGA in optimizing the parameters of the cantilevered beam structure. The optimization results show that the function value obtained with the proposed MOIRCGA is superior to that of RCGA.

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Section: Multi-offspring Improved Real-coded Genetic Algorithm (Momentioning

confidence: 99%

Section: Multi-offspring Improved Real-coded Genetic Algorithm (Momentioning

confidence: 99%

Section: Multi-offspring Improved Real-coded Genetic Algorithm (Momentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

Wang

Zhang

Ersoy

et al. 2019

Computational Intelligence and Neuroscience

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show abstract

“…The crossover operator plays an important role in GA and is dubbed as its backbone, therefore, lots of study is still needed for its development [6]. The GA modification in the form of Directed-Based Crossover (DBX) is conducted to obtain a better population in each generation [7]. Study on its comparison such as Order Crossover, Partially Mapped Crossover and Cycle Crossover has been carried out, resulting order crossover to be the most effective algorithm in producing feasible scheduling solutions [8].…”

Section: Introductionmentioning

confidence: 99%

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Fajrin

Fatichah

2020

register. jurnal. ilm. teknologi. sistem. inf.

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A crossover operator is one of the critical procedures in genetic algorithms. It creates a new chromosome from the mating result to an extensive search space. In the course timetabling problem, the quality of the solution is evaluated based on the hard and soft constraints. The hard constraints need to be satisfied without violation while the soft constraints allow violation. In this research, a multi-parent crossover mechanism is used to modify the classical crossover and minimize the violation of soft constraints, in order to produce the right solution. Multi-parent order crossover mechanism tends to produce better chromosome and also prevent the genetic algorithm from being trapped in a local optimum. The experiment with 21 datasets shows that the multi-parent order crossover mechanism provides a better performance and fitness value than the classical with a zero fitness value or no violation occurred. It is noteworthy that the proposed method is effective to produce available course timetabling.

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Improving Genetic Algorithm to Attain Better Routing Solutions for Real-World Water Line System

Mostafa

Juman

Nawi

et al. 2022

Recent Advances in Soft Computing and Data Mining

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An improved genetic algorithm for numerical function optimization

Cited by 83 publications

References 37 publications

An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

An Improved Real-Coded Genetic Algorithm Using the Heuristical Normal Distribution and Direction-Based Crossover

Multi-parent order crossover mechanism of genetic algorithm for minimizing violation of soft constraint on course timetabling problem

Improving Genetic Algorithm to Attain Better Routing Solutions for Real-World Water Line System

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