Instructed-crossover genetic algorithm based on gradient information

Liang, Ximing; Xiao, Wei; Long, Wen; Qin, Haoyu

doi:10.3724/sp.j.1087.2010.02582

Cited by 3 publications

(3 citation statements)

References 1 publication

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“…At present, some studies have incorporated the conjugate gradient method into the evolutionary algorithm. For example, [17,18] proposed the conjugate gradient method as a search operator to improve the search speed of the algorithm. In [17], the conjugate gradient method is used to search all individuals in the population, which results in excessive computation and a loss of speed advantage.…”

Section: The Conjugate Gradientmentioning

confidence: 99%

“…For example, [17,18] proposed the conjugate gradient method as a search operator to improve the search speed of the algorithm. In [17], the conjugate gradient method is used to search all individuals in the population, which results in excessive computation and a loss of speed advantage. In [18], in order to reduce the computational complexity, the population central individual (population mean individual) is used as the initial search point.…”

Section: The Conjugate Gradientmentioning

confidence: 99%

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Multi‐Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

Ming

Yang

et al. 2019

Mathematical Problems in Engineering

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This study proposes a path-finding model for multi-target strike planning. The model evaluates three elements, i.e., the target value, the aircraft’s threat tolerance, and the battlefield threat, and optimizes the striking path by constraining the balance between mission execution and the combat survival. In order to improve the speed of the Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), we use the conjugate gradient method for optimization. A Gaussian perturbation is added to the search points to make their distribution closer to the population distribution. The simulation shows that the proposed method effectively chooses its target according to the target value and the aircraft’s acceptable threat value, completes the strike on high value targets, evades threats, and verifies the feasibility and effectiveness of the multi-objective optimization model.

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Section: The Conjugate Gradientmentioning

confidence: 99%

Section: The Conjugate Gradientmentioning

confidence: 99%

Multi‐Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

Ming

Yang

et al. 2019

Mathematical Problems in Engineering

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“…Currently, some scholars have conducted research in this area. Reference [6] proposed genetic algorithm based on gradient information to guide cross. By determining the current direction of steepest descent of individuals in the population, so that individuals operate in the effective range of cross-determined.…”

Section: The Analysis Of Hybrid Genetic Algorithmmentioning

confidence: 99%

Improved Method of Hybrid Genetic Algorithm

Ding

Wang

Zheng

et al. 2014

AMM

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On account of low convergence of the traditional genetic algorithm in the late,a hybrid genetic algorithm based on conjugate gradient method and genetic algorithm is proposed.This hybrid algorithm takes advantage of Conjugate Gradient’s certainty, but also the use of genetic algorithms in order to avoid falling into local optimum, so it can quickly converge to the exact global optimal solution. Using Two test functions for testing, shows that performance of this hybrid genetic algorithm is better than single conjugate gradient method and genetic algorithm and have achieved good results.

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Parameter estimation for chaotic system based on evolution algorithm with hybrid crossover

Long¹,

Jiao²

2012

Acta Phys. Sin.

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A hybrid-crossover-based evolution algorithm is proposed to estimate the parameters of chaotic system. Through establishing an appropriate fitness function, the parameter estimation problem is coverted into a multi-dimensional functional optimization problem. In this approach, the individual generation based on good-point-set method is introduced into the evolutionary algorithm initial step, which reinforces the stability and global exploration ability of the evolutionary algorithm. In the evolution process, it not only can be explored to induce the new individuals generated by stochastic hybrid crossover operation to fly into the better subspace, but also can avoid the premature convergence and speed up the convergence. It coordinates the exploitation ability and the exploration ability of algorithm. Numerical simulations on the benchmark function and the Lorenz system are conducted. The results demonstrate the effectiveness of the proposed algorithm, which is shown to be an effective method of parameter estimation for chaotic systems.

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Instructed-crossover genetic algorithm based on gradient information

Cited by 3 publications

References 1 publication

Multi‐Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

Multi‐Target Strike Path Planning Based on Improved Decomposition Evolutionary Algorithm

Improved Method of Hybrid Genetic Algorithm

Parameter estimation for chaotic system based on evolution algorithm with hybrid crossover

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