Parallel implementation of genetic algorithm for searching optimal parameters of artificial neural networks

Nugroho, Eko; Wibowo, Moh Edi; Pulungan, Reza

doi:10.1109/icstc.2017.8011867

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Table 1 shows the general characteristics of the data sample. Table 2 compares the performance of ANN synthesis of the developed method with the usual PGA [12] and the parallel implementation of the NeuroEvolution of Augmenting Topologies (NEAT) Method [13]. Graph in Fig.…”

Section: Research Resultsmentioning

confidence: 99%

Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis

Leoshchenko

Oliinyk

Субботин

et al. 2020

TAPR

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The paper proposes the ways to apply swarm intelligence strategies to parallelize neuroevolution methods for synthesizing artificial neural networks. The proposed approaches will solve a number of problems that usually arise during designing high-performance computing related to the synthesis of neural networks. The object of research is the process of developing a parallel approach for the neuroevolution synthesis of artificial neural networks, namely, the use of swarm intelligence strategies to solve a number of problems in designing a method that would use the resources of a parallel computer system. One of the most problematic areas is the highly adaptive nature and significant operating time of neuroevolution methods. One way to solve these problems is to use parallel computer systems and distributed computing. However, a number of questions arise when designing a parallel neuroevolution method. During research a number of tasks were solved, which included the analysis and study of neuroevolution methods for synthesizing artificial neural networks and problems of their parallelization. Attention is also paid to swarm intelligence methods, which have gained popularity recently and show good results. The new method developed during the work was based on strategies for organizing work with swarm particles. Thus, sub-populations distributed between threads and individuals were analyzed as individual particles that interact with each other and depend on the local environment. Classical genetic operators were modified by criterion mechanisms to improve adaptability. During the experiments, the developed method was compared with classical methods. During the work, special attention was paid not only to the characteristics of the resulting neuromodels, but also to the load on the processor during Operation. The developed method showed acceptable results for all comparisons. The new approach has significantly improved the quality level of the parallel neuroevolution synthesis method, allowing to evenly use the capabilities of computing nodes in a parallel system.

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Section: Research Resultsmentioning

confidence: 99%

Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis

Leoshchenko

Oliinyk

Субботин

et al. 2020

TAPR

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“…Simulation of the structural-parametric assessment method was made in accordance with the algorithm in Fig. 1 and expressions ( 6)- (27). The simulation of the proposed evaluation method was carried out in the MathCad 14 (USA) software environment.…”

Section: Practical Verification Of the Proposed Methods In The Analysis Of The Operational Situation Of The Troops (Forces) Groupingmentioning

confidence: 99%

“…N 0 is the number of outputs of the dynamic system; s j , j N = 1 0 , is the sample size for each output of the dynamic system; y i j , i s j = 1, , j N = 1 0 , are the measurements of the outputs that form the samples; t i j , i Criterion ( 6) is similar to the criterion for a single-input and single-output problem as (3). However, criterion (4) can also be generalized in the case of multiple input-output systems [27][28][29][30].…”

Section: Formalized Description Of Structural and Parametric Assessment Of The Object Statementioning

confidence: 99%

Development of a method of structural-parametric assessment of the object state

Mahdi¹,

Zhyvotovskyi

Kravchenko

et al. 2021

EEJET

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A method of structural and parametric assessment of the object state has been developed. The essence of the method is to provide an analysis of the current state of the object under analysis. The key difference of the developed method is the use of advanced procedures for processing undefined initial data, selection, crossover, mutation, formation of the initial population, advanced procedure for training artificial neural networks and rounding coordinates. The use of the method of structural-parametric assessment of the object state allows increasing the efficiency of object state assessment. An objective and complete analysis is achieved using an advanced algorithm of evolution strategies. The essence of the training procedure is the training of synaptic weights of the artificial neural network, the type and parameters of the membership function, the architecture of individual elements and the architecture of the artificial neural network as a whole. An example of using the proposed method in assessing the operational situation of the troops (forces) grouping is given. The developed method is 30–35 % more efficient in terms of the fitness of the obtained solution compared to the conventional algorithm of evolution strategies. Also, the proposed method is 20–25 % better than the modified algorithms of evolution strategies due to the use of additional improved procedures according to the criterion of fitness of the obtained solution. The proposed method can be used in decision support systems of automated control systems (artillery units, special-purpose geographic information systems). It can also be used in DSS for aviation and air defense ACS, DSS for logistics ACS of the Armed Forces of Ukraine

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“…Through the study explores the implementation of a parallel GA for the search for optimal specifications of artificial neural networks system. The Palatalization is accomplished using the Interprocesses communication Framework, wherein sub-populations are shared among processors during selection, mutation, and crossover [35].…”

Section: Parallelizationmentioning

confidence: 99%

Genetic Algorithm: Reviews, Implementations, and Applications

et al. 2020

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Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as adaptive technology to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

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Parallel implementation of genetic algorithm for searching optimal parameters of artificial neural networks

Cited by 9 publications

References 15 publications

Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis

Usage of swarm intelligence strategies during projection of parallel neuroevolution methods for neuromodel synthesis

Development of a method of structural-parametric assessment of the object state

Genetic Algorithm: Reviews, Implementations, and Applications

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