Equivalence of probabilistic tournament and polynomial ranking selection

Hingee, Kassel; Hutter, Marcus

doi:10.1109/cec.2008.4630852

Cited by 15 publications

(11 citation statements)

References 7 publications

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“…Generally, the obtained objective function from the NLP sub‐problem must be considered. However, the method suggested in [31] is used here to replace the method based on assigning the penalty function to the problem. Using the binary tournament to select each parent by randomly selecting two players and afterwards, selecting the most desired individual among that sets as a parent (smaller fitness value) [32]. The child is also selected using two binary tournaments to generate a parent. Mating random pairs.…”

Section: Hybrid Icga‐nlp Algorithmmentioning

confidence: 99%

Hybrid mixed‐integer non‐linear programming approach for directional over‐current relay coordination

Javadi

Nezhad

Anvari-Moghadam

et al. 2019

J. eng.

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Section: Hybrid Icga‐nlp Algorithmmentioning

confidence: 99%

Hybrid mixed‐integer non‐linear programming approach for directional over‐current relay coordination

Javadi

Nezhad

Anvari-Moghadam

et al. 2019

J. eng.

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“…Next, programs are selected for reproduction following the principle of Darwin's survival of the fittest. Selection methods for reproduction are numerous, but for this work, we briefly explain tournament selection [Hingee and Hutter 2008]. This method is a variant of rank-based selection methods, in which individuals are randomly selected and then ranked according to their relative fitness value, selecting the fittest among them for reproduction.…”

Section: Introductionmentioning

confidence: 99%

A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures

Diniz¹,

Cordeiro²,

Miranda³

et al. 2018

Anais Do XV Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2018)

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Deep Learning is a research area under the spotlight in recent years due to its successful application to many domains, such as computer vision and image recognition. The most prominent technique derived from Deep Learning is Convolutional Neural Network, which allows the network to automatically learn representations needed for detection or classification tasks. However, Convolutional Neural Networks have some limitations, as designing these networks are not easy to master and require expertise and insight. In this work, we present the use of Genetic Algorithm associated to Grammar-based Genetic Programming to optimize Convolution Neural Network architectures. To evaluate our proposed approach, we adopted CIFAR-10 dataset to validate the evolution of the generated architectures, using the metric of accuracy to evaluate its classification performance in the test dataset. The results demonstrate that our method using Grammar-based Genetic Programming can easily produce optimized CNN architectures that are competitive and achieve high accuracy results.

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“…Mutation: Based on Grammar-Based Mutation (GBM)[73] and a variation of it of our own design, Schema Diversity Factor (SDF) mutation.• Probabilistic Tournament Selection[74] using the formula from[75].• Crossover: Whingham (WX) [70]. • Generational population replacement.…”

mentioning

confidence: 99%

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

Castillo¹,

Díaz-Álvarez²,

Callejo³

et al. 2020

Preprint

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Photogrammetry involves aerial photography of the earth’s surface and subsequently processing the images to provide a more accurate depiction of the area (Orthophotography). It’s used by the Spanish Instituto Geográfico Nacional to update road cartography but requires a significant amount of manual labor due to the need to perform visual inspection of all tiled images. Deep Learning techniques (artificial neural networks with more than one hidden layer) can perform road detection but it is still unclear how to find the optimal network architecture. Our system applies grammar guided genetic programming to the search of deep neural network architectures. In this kind of evolutive algorithm all the population individuals (here candidate network architectures) are constrained to rules specified by a grammar that defines valid and useful structural patterns to guide the search process. Grammar used includes well-known complex structures (e.g. Inception-like modules) combined with a custom designed mutation operator (dynamically links the mutation probability to structural diversity). Pilot results show that the system is able to design models for road detection that obtain test accuracies similar to that reached by state of the art models when evaluated over a dataset from the Spanish National Aerial Orthophotography Plan.

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Equivalence of probabilistic tournament and polynomial ranking selection

Cited by 15 publications

References 7 publications

Hybrid mixed‐integer non‐linear programming approach for directional over‐current relay coordination

Hybrid mixed‐integer non‐linear programming approach for directional over‐current relay coordination

A Grammar-based Genetic Programming Approach to Optimize Convolutional Neural Network Architectures

Grammar Guided Genetic Programming for Network Architecture Search and Road Detection on Aerial Orthophotography

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