Cultured differential evolution for constrained optimization

Becerra, Ricardo Landa; Coello, Carlos A. Coello

doi:10.1016/j.cma.2005.09.006

Cited by 228 publications

(81 citation statements)

References 13 publications

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“…The other important property of this algorithm is a local limitation of selection operator only to the two individuals: parent (x i ) and child (u i ), and due to this property the selection operator is more effective and faster [12].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…The individuals occurring in this algorithm are represented by real number strings. Its searching space must be continuous [10], [12].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…The differential evolution algorithm, by computation of difference between two randomly chosen individuals from the population, determines a function gradient in a given area (not in a single point), and therefore prevents sticking the solution in a local extremum of optimized function [10], [12]. The other important property of this algorithm is a local limitation of selection operator only to the two individuals: parent (x i ) and child (u i ), and due to this property the selection operator is more effective and faster [12].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…Differential evolution algorithm has been introduced recently (in the year 1997), and is a heuristic algorithm for global optimization. Its advantages are as follows: a possibility of finding the global minimum of a multi-modal function regardless of initial values of its parameters, quick convergence and a small number of parameters to set up at the start of the algorithm operation [12].…”

Section: Introductionmentioning

confidence: 99%

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Training of artificial neural networks using differential evolution algorithm

Słowik

Bialko

2008

2008 Conference on Human System Interactions

110

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-In the paper an application of differential evolution algorithm to training of artificial neural networks is presented. The adaptive selection of control parameters has been introduced in the algorithm; due to this property only one parameter is set at the start of proposed algorithm. The artificial neural networks to classification of parity-p problem have been trained using proposed algorithm. Results obtained using proposed algorithm have been compared to the results obtained using other evolutionary method, and gradient training methods such as: error back-propagation, and Levenberg-Marquardt method. It has been shown in this paper that application of differential evolution algorithm to artificial neural networks training can be an alternative to other training methods.Keywords -artificial intelligence, artificial neural network, differential evolution algorithm, training method.

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Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…The individuals occurring in this algorithm are represented by real number strings. Its searching space must be continuous [10], [12].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Section: Differential Evolution Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Training of artificial neural networks using differential evolution algorithm

Słowik

Bialko

2008

2008 Conference on Human System Interactions

110

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show abstract

“…It was introduced several years ago (in 1997) and has been developed intensively in recent years [9]. Its advantages are as follows: the possibility of finding the global minimum of a multimodal function regardless of the initial values of its parameters, quick convergence, and the small number of parameters that needs to be set up at the start of the algorithm's operation [10]. Since 1997, the DE algorithm has been modified to increase its effectiveness .…”

Section: Introductionmentioning

confidence: 99%

Medical Diagnosis Classification Using Migration Based Differential Evolution Algorithm

Thein¹,

Tun²

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Constructing a classification model is important in machine learning for a particular task. A classification process involves assigning objects into predefined groups or classes based on a number of observed attributes related to those objects. Artificial neural network is one of the classification algorithms which, can be used in many application areas. This paper investigates the potential of applying the feed forward neural network architecture for the classification of medical datasets. Migration based differential evolution algorithm (MBDE) is chosen and applied to feed forward neural network to enhance the learning process and the network learning is validated in terms of convergence rate and classification accuracy. In this paper, MBDE algorithm with various migration policies is proposed for classification problems using medical diagnosis.

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Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems

Kashani

Chiong

Dhakal

et al. 2021

Engineering Reports

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The interior search algorithm (ISA) is an optimization algorithm inspired by esthetic techniques used for interior design and decoration. The algorithm has only one parameter, controlled by θ, and uses an evolutionary boundary constraint handling (BCH) strategy to keep itself within an admissible solution space while approaching the optimum. We apply the ISA to find optimal weight design of truss structures with frequency constraints. Sensitivity of the ISA's performance to the θ parameter and the BCH strategy is investigated by considering different values of θ and BCH techniques. This is the first study in the literature on the sensitivity of truss optimization problems to various BCH approaches. Moreover, we also study the impact of different BCH methods on diversification and intensification. Through extensive numerical simulations, we identified the best BCH methods that provide consistently better results for all truss problems studied, and obtained a range of θ that maximizes the ISA's performance for all problem classes studied. However, results also recommend further fine-tuning of parameter settings for specific case studies to obtain the best results.

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Cultured differential evolution for constrained optimization

Cited by 228 publications

References 13 publications

Training of artificial neural networks using differential evolution algorithm

Training of artificial neural networks using differential evolution algorithm

Medical Diagnosis Classification Using Migration Based Differential Evolution Algorithm

Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems

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