Comparison of Crossover in Genetic Algorithm for Discrete-Time System Identification

Zainuddin, Farah Ayiesya; Samad, Fahmi Abd

doi:10.15866/ireme.v15i2.19726

Cited by 13 publications

(11 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The quality of the chromosome can be improved by the selection process which depends on the chromosome's fitness value. We apply a simple and effective selection operator which is the tournament selection method [24], [25]. To produce high-quality offspring, crossover, and mutation operators, as well as their probabilities, are very important.…”

Section: Proposed Framework/methodsmentioning

confidence: 99%

A hybrid approach to enhanced genetic algorithm for route optimization problems

Chattoraj

Vinayakamurthy

2023

IJEECS

View full text Add to dashboard Cite

Shortest path problem has emerged to be one of the significant areas of research and there are various algorithms involved in it. One of the successful optimization techniques is genetic algorithm (GA). This paper proposes an efficient hybrid genetic algorithm where initially we use a map reduction technique to the graph and then find the shortest path using the conventional genetic algorithm with an improved crossover operator. On comparing this hybrid algorithm with other algorithms, it has been detected that the performance of the modified genetic algorithm is better as comparison to the other methods in terms of various metrics used for the evaluation.

show abstract

Section: Proposed Framework/methodsmentioning

confidence: 99%

A hybrid approach to enhanced genetic algorithm for route optimization problems

Chattoraj

Vinayakamurthy

2023

IJEECS

View full text Add to dashboard Cite

show abstract

“…The control transition point x was taken as a gene, and all the control transition points in the operation interval were sorted and combined into one chromosome.(2) Initial population: The initial population with the size set at N was obtained through random generation. That is, the ( x 1 … x m ) arranged in sequence was randomly generated between (0, S ).(3) Selection operator: In this paper, truncation selection was adopted to avoid the repeated selection of individuals with small fitness values in the population, and to prevent the local convergence resulting from a gradually dwindling population in the iterative process.(4) Crossover operator: An arithmetic crossover operator 20 was adopted in this paper because the chromosomes were encoded by real numbers.(5) Mutation operator: Real-number uniform mutation was performed, where random numbers conforming to the uniform distribution in a certain range were utilized, and the original gene value of each locus in the individual code string was replaced with a relatively small probability. Random numbers were generated with two adjacent control transition points as upper and lower bounds to avoid the disorder of individual control transition points after the chromosome mutation.(6) Population screening operator: The control transition point corresponding to each chromosome was substituted into a dynamic model of train operation.…”

Section: Improved Agamentioning

confidence: 99%

“…(4) Crossover operator: An arithmetic crossover operator 20 was adopted in this paper because the chromosomes were encoded by real numbers.…”

Section: Improved Agamentioning

confidence: 99%

On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

He,

Qiao,

Zhang

2023

Proceedings of the Institution of Mechanical Engineers, Part F:

View full text Add to dashboard Cite

On-time and energy-saving train operation is important for the sustainable development of rail transit. As for the problems of traction energy consumption and on-time arrival at stations faced by trains in rail transit, an optimization strategy of energy-saving speed curves of trains based on an improved adaptive genetic algorithm (AGA) was proposed in this paper. First, weight coefficients of operation time and energy consumption were designed through an analytic hierarchy process, and an optimization model that targets train operation time and energy consumption was established according to a basic train operation model with constraints such as speed limits and precise train stopping. Then, on-time and energy-saving speed curves of trains were generated based on the improved AGA. Finally, a simulation was carried out with actual rail transit lines. The results show that the proposed method has strong efficiency for energy conservation and better optimization performance than the simple genetic algorithm in solving train trajectory optimization problem.

show abstract

“…It does not fall into the trap of local superiority, and can find the global optimum among many local superiorities, which is a global optimization method [21]. In recent years, genetic algorithms have been used in many fields internationally [22][23][24][25][26][27].…”

Section: Intelligent Genetic Algorithmmentioning

confidence: 99%

Early warning and detection of geological disasters based on intelligent genetic algorithm

Sun

Zhang

Liao

2023

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

In recent years, the frequent occurrence of earthquakes, landslides, debris flow and other geological disasters worldwide is endangering people's production and life, which not only causes serious damage to infrastructure, but also creates a certain degree of fear for people. Geological disaster is an open nonlinear complex system, which has extraordinary complex geological process, formation conditions, and causes. Therefore, it makes difficulty in capturing the dynamic information and searching for the global optimal solution. Meanwhile, traditional geological disaster warning system has the deficiencies of single disaster warning and low accuracy. In order to improve the level of early warning and detection of geological disasters, this paper combined the genetic algorithm with superior performance and Support Vector Regression (SVR) algorithm to establish a feasible and credible early warning and monitoring model for geological disasters. The experimental results show that the early warning and monitoring model proposed in this paper can greatly improve the ability of geological disaster prevention and early warning, and greatly improve the level of disaster prevention and early warning, with good engineering application value.

show abstract

Comparison of Crossover in Genetic Algorithm for Discrete-Time System Identification

Cited by 13 publications

References 0 publications

A hybrid approach to enhanced genetic algorithm for route optimization problems

A hybrid approach to enhanced genetic algorithm for route optimization problems

On-time and energy-saving train operation strategy based on improved AGA multi-objective optimization

Early warning and detection of geological disasters based on intelligent genetic algorithm

Contact Info

Product

Resources

About