An improved evolution algorithm using population competition genetic algorithm and self-correction BP neural network based on fitness landscape

Yang, Jing; Hu, Yingpeng; Zhang, Kaixi; Wu, Yanghui

doi:10.1007/s00500-020-05250-7

Cited by 39 publications

(14 citation statements)

References 13 publications

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“…Artificial neural network (ANN) is an arithmetic model established by humans to deal with the function and structure of the brain [20][21][22]. It has similar functions to the human brain.…”

Section: Eoretical Basis Of Neural Networkmentioning

confidence: 99%

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Liu

Guang-ping

2021

Computational Intelligence and Neuroscience

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In the 21st century, with the increasingly urgent requirements for lightweight in the fields of aviation, aerospace, and electronics, especially automobiles, many properties of magnesium alloy materials, especially the low-density performance characteristics, have been widely valued. In order to better use magnesium metal materials, it is very important to evaluate its mechanical properties. This article is based on 196 sets of mechanical performance experimental results and related data of AZ31 and AZ91 2 magnesium alloys. Based on data analysis and sorting, take deformation temperature, deformation rate, deformation coefficient, solid solution temperature, and solid solution time as input and take ultimate tensile strength (UTS), yield strength (YS), and elongation (ELO) as output. The 5-8-1 three-layer BP neural network forecast model optimized by the genetic algorithm is used for data training. The training results show that the prediction model can accurately predict the tensile strength, yield strength, and elongation. Compared with the general BP neural network prediction model, the BP neural network based on the genetic algorithm has small discreteness and high fitness: the average error of UTS and YS of AZ31 magnesium alloy is reduced to 0.88% and 3.3%, respectively. The most obvious is that the elongation of AZ31 ELO is reduced, and the error is reduced to 8.1%.

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Section: Eoretical Basis Of Neural Networkmentioning

confidence: 99%

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Liu

Guang-ping

2021

Computational Intelligence and Neuroscience

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“…In Fig. 15, a convergence process of BNN toward local optimum solution and prevention of global solution was a big concern (Chandwani et al 2015;Yang et al 2021). Applying the extreme learning machine (ELM) as another option can alleviate the convergence concern with the local minima and contribute further easiness as there are no stopping criteria and learning rates are needed (Christou et al 2019;Sussner and Campiotti 2010).…”

Section: Discussion and Interpretationmentioning

confidence: 99%

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Ahmed

Ali

2022

ACT

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Recycled aggregate concrete (RAC) has attracted more interesting in the past several years because it is an economical and eco-friendly building material. But generally, the mechanical properties of RAC are poor compared to natural aggregate concrete (NAC). So, the mechanical properties of RAC need robust predictive models to be evaluated before its application. Traditional (empirical based) models, e.g., linear, and non-linear regression methods, have been extensively proposed. But these models lack flexibility in updating (i.e., limited to a finite number of variables) and can give inaccurate results. Consequently, to handle such shortcomings, several Artificial Intelligence (AI) models have been suggested as an alternative strategy for predicting the mechanical properties of RAC. In this study, state-of-the-art AI models were reviewed to predict the mechanical properties of RAC. The application of each predictive model and its training, testing, and performance are critically examined and analysed, consequently identifying present knowledge gaps, practical recommendations, and required future investigation.

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“…Takagi [24] proposed a method of estimating the convergence point using the solutions of different generations and used this convergence point to accelerate the search process. Yang [25] presented a genetic algorithm back-propagation neural network algorithm that uses the FL to improve solutions. They use the fitness landscape analysis to optimize the learning rate of the back-propagation algorithm, and the GA evolves a population of solutions.…”

Section: Related Workmentioning

confidence: 99%

Local Fitness Landscape Exploration Based Genetic Algorithms

et al. 2023

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Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, "Fitness Landscape Exploration based Genetic Algorithm" (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEXbased algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

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An improved evolution algorithm using population competition genetic algorithm and self-correction BP neural network based on fitness landscape

Cited by 39 publications

References 13 publications

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

[Retracted] Evaluation of Mechanical Properties of Materials Based on Genetic Algorithm Optimizing BP Neural Network

Artificial Intelligence Models for Predicting Mechanical Properties of Recycled Aggregate Concrete (RAC): Critical Review

Local Fitness Landscape Exploration Based Genetic Algorithms

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