No Free Lunch Theorem: A Review

Adam, Stavros P.; Alexandropoulos, Stamatios-Aggelos N.; Pardalos, Pãnos M.; Vrahatis, Michael N.

doi:10.1007/978-3-030-12767-1_5

Cited by 254 publications

(116 citation statements)

References 44 publications

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“…Many researchers used these algorithms to solve complex optimization problems in different fields (Hoursan et al 2020;Tirkolaee et al 2020a, b;Sangaiah et al 2020;Lotfi et al 2018Lotfi et al , 2019Lotfi et al , 2020Zare Mehrjerdi and Lotfi 2019;Khalilpourazari et al 2020c;Hashemi Doulabi et al 2020a, b). Defined by the no free lunch (NFL) theorem, we can logically prove that no single method performs optimally in resolving all problems (Adam et al 2019;Wolpert and Macready 1997). Any metaheuristic algorithm may perform well in some benchmarks but weak in others.…”

Section: Survey On Research Conductedmentioning

confidence: 99%

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

Khalilpourazari

Doulabi

2021

Ann Oper Res

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World Health Organization (WHO) stated COVID-19 as a pandemic in March 2020. Since then, 26,795,847 cases have been reported worldwide, and 878,963 lost their lives due to the illness by September 3, 2020. Prediction of the COVID-19 pandemic will enable policymakers to optimize the use of healthcare system capacity and resource allocation to minimize the fatality rate. In this research, we design a novel hybrid reinforcement learning-based algorithm capable of solving complex optimization problems. We apply our algorithm to several well-known benchmarks and show that the proposed methodology provides quality solutions for most complex benchmarks. Besides, we show the dominance of the offered method over state-of-the-art methods through several measures. Moreover, to demonstrate the suggested method's efficiency in optimizing real-world problems, we implement our approach to the most recent data from Quebec, Canada, to predict the COVID-19 outbreak. Our algorithm, combined with the most recent mathematical model for COVID-19 pandemic prediction, accurately reflected the future trend of the pandemic with a mean square error of 6.29E−06. Furthermore, we generate several scenarios for deepening our insight into pandemic growth. We determine essential factors and deliver various managerial insights to help policymakers making decisions regarding future social measures.

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Section: Survey On Research Conductedmentioning

confidence: 99%

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

Khalilpourazari

Doulabi

2021

Ann Oper Res

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“…The reason for using multiple classification techniques is due to the 'No Free Lunch Theorem'. The performance of one classification technique can be overthrown by another [32]. The details of the classification methods and their kernels are available in [33][34][35][36][37].…”

Section: Short-time Fourier Transformmentioning

confidence: 99%

Hybrid Computerized Method for Environmental Sound Classification

et al. 2020

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Classification of environmental sounds plays a key role in security, investigation, robotics since the study of the sounds present in a specific environment can allow to get significant insights. Lack of standardized methods for an automatic and effective environmental sound classification (ESC) creates a need to be urgently satisfied. As a response to this limitation, in this paper, a hybrid model for automatic and accurate classification of environmental sounds is proposed. Optimum allocation sampling (OAS) is used to elicit the informative samples from each class. The representative samples obtained by OAS are turned into the spectrogram containing their time-frequency-amplitude representation by using a short-time Fourier transform (STFT). The spectrogram is then given as an input to pre-trained AlexNet and Visual Geometry Group (VGG)-16 networks. Multiple deep features are extracted using the pre-trained networks and classified by using multiple classification techniques namely decision tree (fine, medium, coarse kernel), k-nearest neighbor (fine, medium, cosine, cubic, coarse and weighted kernel), support vector machine, linear discriminant analysis, bagged tree and softmax classifiers. The ESC-10, a ten-class environmental sound dataset, is used for the evaluation of the methodology. An accuracy of 90.1%, 95.8%, 94.7%, 87.9%, 95.6%, and 92.4% is obtained with a decision tree, k-neared neighbor, support vector machine, linear discriminant analysis, bagged tree and softmax classifier respectively. The proposed method proved to be robust, effective, and promising in comparison with other existing state-of-the-art techniques, using the same dataset. INDEX TERMS Environmental sound classification, Optimal allocation sampling, spectrogram, convolutional neural network, classification techniques.

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“…The unappealing selection of different parameters, visibility, etc., is also blamed. There is an inadequacy of chance that every optimal algorithm will be available to solve all sorts of problems, according to No free lunch theorem [2].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

Kumar¹,

Ganesh²,

Bhoopal³

et al. 2021

ISI

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This paper presents a variety of contemporary optimization techniques inspired by the real life in nature. Optimization reveals substantial developments in computing systems as well as has come to be the most encouraging strategy for several design applications. The study is conducted on single-objective, multi-objective, and hybrid optimization strategies. These optimization schemes will be of excellent help to organizations to identify optimum criteria and to improve process as well as product high quality. For selected optimization strategies, the process of formulating the objective function/stiffness function for a minimal issue exists. Over the last few years, the most combinatoric problems of all traditional optimization approaches were solved by using metaheuristic algorithms to have optimal solutions for real-time applications. This paper discussed some of the important and feasible optimization scheme and the related algorithms and approaches.

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No Free Lunch Theorem: A Review

Cited by 254 publications

References 44 publications

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

Designing a hybrid reinforcement learning based algorithm with application in prediction of the COVID-19 pandemic in Quebec

Hybrid Computerized Method for Environmental Sound Classification

Evolutionary Algorithms for Real Time Engineering Problems: A Comprehensive Review

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