A multi-objective micro genetic ELM algorithm

Lahoz, David; Lacruz, Beatriz; Mateo, Pedro M.

doi:10.1016/j.neucom.2012.11.035

Cited by 19 publications

(10 citation statements)

References 15 publications

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“…It is reported that this approach is useful for systems identification tasks. In [21] a multi-objective micro-genetic extreme learning machine (G-ELM) is proposed, which provides the appropriate number of hidden nodes in the machine for solving the problem and minimizes the mean square error (MSE) of the training phase. The micro-GA is applied successfully for many applications such as designing wave-guide slot antenna with dielectric lenses [36], detection of flaws in composites [30], and scheduling of a real-world pipeline network [29], where better performances compared to the standard GA are reported.…”

Section: Outline Of the Thesismentioning

confidence: 99%

“…Related Research Works Genetic Algorithm (GA) [5], [10], [21], [28]- [36] Particle Swarm Optimization (PSO) [6], [7], [40]- [52], [58] Differential Evolution (DE) [1], [3], [8], [9], [11]- [18], [60] Artificial Bee Colony (ABC) [53] Bacterial Foraging Optimization (BFO) [54] Artificial Immune System (AIS) [56] Elitistic Evolution (EEv) [57] has outperformed the standard bacterial foraging optimization algorithm (BFOA) with a larger population size [54]. For the environmental economic dispatch case study, a chaotic micro-bacterial foraging algorithm (CMBFA) with a time-varying chemotactic step size is proposed in [55].…”

Section: Population-based Algorithmmentioning

confidence: 99%

“…Wilcoxon rank-sum results by considering the MDEVM as the reference algorithm are presented in Table A. 21. Summary of the performance results are presented in Table 5.7.…”

Section: Experimental Series 5: Number Of Function Calls Analysismentioning

confidence: 99%

See 2 more Smart Citations

Micro-differential evolution: Diversity enhancement and a comparative study

Salehinejad

Rahnamayan

Tizhoosh

2017

Applied Soft Computing

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Section: Outline Of the Thesismentioning

confidence: 99%

Section: Population-based Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Micro-differential evolution: Diversity enhancement and a comparative study

Salehinejad

Rahnamayan

Tizhoosh

2017

Applied Soft Computing

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“…Then Coello and Polido had extended the µGA to consider multi-objected optimization problem [7]. After that many methods developed which utilized from µGA to solve numerous problems [8]- [16]. Shin-Yeu Lin and Hsinng-Fang Tsai [8] proposed a µGA which consists of spatial crossover and correction schemes to solve the constrained threedimensional reader network planning.…”

Section: Introductionmentioning

confidence: 99%

A New Micro Genetic Algorithm Based Image Stitching Approach for Camera Arrays at Production Lines

Yetiş¹,

Bayğın²,

Karaköse³

2017

JOIG

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Nowadays, using image processing techniques on the image taken by camera at the top of production line for detection of faulty products is widespread. The quality of images can decline due to the distance between camera and production line while trying to get whole image of the bigger products. Therefore, in this study we employed camera array instead of only one camera and placed it at a nearer position to production line. As the images taken from cameras in camera array contain specific parts of the whole product, it is required to stitch images and obtain single image of the product before analyze it. Production line speed or the product position can be vary so it is required to find the optimal stitching points in order to get single image. As an optimization technique micro genetic algorithm (µGA) which refers to Genetic Algorithm (GA) with small population size and re-initialization process is popular for its proper and fast solutions. In this paper, we have developed a method using µGA known by its high convergence rate and low termination possibility at a local optimum to be used in image stitching process. Using µGA known by its high convergence rate, less need of computational source and rare premature solution provided us optimal and fast solutions. Experimental results show that µGA, which outperforms conventional GA, gives good results in a reasonable time and it can be used in image stitching process as an alternative.

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“…The improved version of nondominated sorting genetic algorithm (NSGA-II) with a specific population initialization strategy are embedded into the standard micro-GA to solve the MOO problems [10]. In [21] a multi-objective micro genetic extreme learning machine (G-ELM) was proposed, which provides the appropriate number of hidden nodes in the machine for solving the problem as well as the tuned values of weights and biases, which minimizes the mean square error (MSE) of results.…”

Section: Introductionmentioning

confidence: 99%

Micro-differential evolution with vectorized random mutation factor

Salehinejad

Rahnamayan

Tizhoosh

et al. 2014

2014 IEEE Congress on Evolutionary Computation (CEC)

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One of the main disadvantages of population-based evolutionary algorithms (EAs) is their high computational cost due to the nature of evaluation, specially when the population size is large. The micro-algorithms employ a very small number of individuals, which can accelerate the convergence speed of algorithms dramatically, while it highly increases the stagnation risk. One approach to overcome the stagnation problem can be increasing the diversity of the population. To do so, a microdifferential evolution with vectorized random mutation factor (MDEVM) algorithm is proposed in this paper, which utilizes the small size population benefit while preventing stagnation through diversification of the population. The proposed algorithm is tested on the 28 benchmark functions provided at the IEEE congress on evolutionary computation 2013 (CEC-2013). Simulation results on the benchmark functions demonstrate that the proposed algorithm improves the convergence speed of its parent algorithm.

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A multi-objective micro genetic ELM algorithm

Cited by 19 publications

References 15 publications

Micro-differential evolution: Diversity enhancement and a comparative study

Micro-differential evolution: Diversity enhancement and a comparative study

A New Micro Genetic Algorithm Based Image Stitching Approach for Camera Arrays at Production Lines

Micro-differential evolution with vectorized random mutation factor

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