GACE: A meta-heuristic based in the hybridization of Genetic Algorithms and Cross Entropy methods for continuous optimization

Parallel heterogeneous genetic algorithms for continuous optimization

Parallel Computing ◽

10.1016/j.parco.2003.12.011 ◽

2004 ◽

Vol 30 (5-6) ◽

pp. 699-719 ◽

Cited By ~ 36

Author(s):

E. Alba ◽

F. Luna ◽

A.J. Nebro ◽

J.M. Troya

Keyword(s):

Genetic Algorithms ◽

Continuous Optimization

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Cross Entropy Method Meets Local Search for Continuous Optimization Problems

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213017500208 ◽

2017 ◽

Vol 26 (06) ◽

pp. 1750020

Author(s):

Xin Zhang ◽

Xiu Zhang

Keyword(s):

Local Search ◽

Controller Design ◽

Optimization Problems ◽

Continuous Optimization ◽

Entropy Method ◽

Cross Entropy ◽

Mathematical Functions ◽

Cross Entropy Method ◽

Pid Controller Design ◽

Continuous Optimization Problems

The effectiveness of cross entropy (CE) method has been investigated on both combinatorial and continuous optimization problems, though it lacks exploitative search to refine solutions. Hybrid with local search (LS) method can greatly improve the performance of evolutionary algorithm. This paper proposes a parameter-less framework combining CE with LS method. Four LS methods are chosen and four combination algorithms are obtained after combining them with the CE method. We first study the performance of the four combinations on a set of twenty eight mathematical functions including both unimodal and multimodal functions. CE hybrid with Powell’s method (CE-Pow) is identified as the most effective algorithm. Then the CE-Pow algorithm is applied to resolve proportional, integral, and derivative (PID) controller design problem and Lennard-Jones potential problem. Its performance has been verified by comparing with four state of the art evolutionary algorithms. Experimental results show that CE-Pow significantly outperforms other benchmark algorithms.

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A Hybrid Method for Short-Term Traffic Congestion Forecasting Using Genetic Algorithms and Cross Entropy

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2015.2491365 ◽

2016 ◽

Vol 17 (2) ◽

pp. 557-569 ◽

Cited By ~ 63

Author(s):

Pedro Lopez-Garcia ◽

Enrique Onieva ◽

Eneko Osaba ◽

Antonio D. Masegosa ◽

Asier Perallos

Keyword(s):

Genetic Algorithms ◽

Hybrid Method ◽

Traffic Congestion ◽

Cross Entropy ◽

Short Term

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Climate change impact assessment on low streamflows using cross-entropy methods

Climate Research ◽

10.3354/cr01674 ◽

2021 ◽

Author(s):

Z Sheikh ◽

A Moghaddam Nia ◽

D Han

Keyword(s):

Climate Change ◽

Impact Assessment ◽

Climate Change Impact ◽

Cross Entropy ◽

Climate Change Impact Assessment ◽

Entropy Methods ◽

Change Impact

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Nonlinear Integer, Discrete and Continuous Optimization Using Adaptive Range Genetic Algorithms

Volume 2: 23rd Design Automation Conference ◽

10.1115/detc97/dac-3728 ◽

1997 ◽

Cited By ~ 2

Author(s):

Masao Arakawa ◽

Ichiro Hagiwara

Keyword(s):

Genetic Algorithms ◽

Combinatorial Optimization ◽

Discrete Optimization ◽

Continuous Optimization ◽

The Other ◽

Computational Time ◽

Test Problems ◽

Design Variables ◽

Discrete And Continuous Optimization

Abstract Genetic algorithms are effective algorithms for large scaled combinatorial optimization. They are potentially effective in integer and discrete optimization. However, as they are not well coded to its tedious expression in converting chromosomes to design variables, we need to do some special efforts to overcome these flaws. In the proposed method, it automatically adapts searching ranges according to the situation of the generation. Thus, we are free from these flaws. Moreover, we don’t have to give too many genes to chromosome, we can save computational time and memory and the convergence becomes better. In this paper, we combine the proposed integer and discrete adaptive range genetic algorithms and adaptive real range genetic algorithms which we presented in the previous studies, and present an extended genetic algorithms method. We applied the proposed method to well-known test problems, compare the results with the other methods and show its effectiveness.

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