A comparative review of approaches to prevent premature convergence in GA

Pandey, Hari Mohan; Chaudhary, Ankit; Mehrotra, Deepti

doi:10.1016/j.asoc.2014.08.025

Cited by 208 publications

(83 citation statements)

References 41 publications

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“…The same procedure was followed to choose the population size, the mutation rate, and the starting temperature for the genetic and simulated annealing algorithms respectively. We considered relevant literature and used comparable methods of selecting the experimental parameters [34] and taking various precautions to avoid premature convergence [35]. The target training error used in all the experiments was set to 0.001.…”

Section: Parameter Settingsmentioning

confidence: 99%

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Hassan

Hamada

2017

Computation

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Accuracy improvement is among the primary key research focuses in the area of recommender systems. Traditionally, recommender systems work on two sets of entities, Users and Items, to estimate a single rating that represents a user's acceptance of an item. This technique was later extended to multi-criteria recommender systems that use an overall rating from multi-criteria ratings to estimate the degree of acceptance by users for items. The primary concern that is still open to the recommender systems community is to find suitable optimization algorithms that can explore the relationships between multiple ratings to compute an overall rating. One of the approaches for doing this is to assume that the overall rating as an aggregation of multiple criteria ratings. Given this assumption, this paper proposed using feed-forward neural networks to predict the overall rating. Five powerful training algorithms have been tested, and the results of their performance are analyzed and presented in this paper.

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Section: Parameter Settingsmentioning

confidence: 99%

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Hassan

Hamada

2017

Computation

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“…The issue of premature convergence, introduced above, is of particular importance in all the GA methodologies (see the very recent paper of Pandey et al (2014), and references within, for a comparative review of approaches). One of the methods devised to avoid premature convergence is known as the Island Model (Whitley, 1994;Whitley et al, 1999) and we implement this method in our code because of its efficiency and its straightforward application when dealing with a parallel environment.…”

Section: Methodsmentioning

confidence: 98%

Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

Cantelli

D'Orta

Cattini

et al. 2015

Atmospheric Environment

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“… Exploitation is based on crossover operator and allows improving initial results (reaching optima with a high accuracy) [18,19].…”

Section: Advances In Computer Science Research (Acsr) Volume 72mentioning

confidence: 99%

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

Alkhasov¹,

Tselykh²

2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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A comparative review of approaches to prevent premature convergence in GA

Cited by 208 publications

References 41 publications

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Performance Comparison of Feed-Forward Neural Networks Trained with Different Learning Algorithms for Recommender Systems

Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

Combined Optimization and Modified Performance Metrics for Automated Model and Parameter Selection in Telecom Customer Churn Prediction

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