A Survey of Automatic Parameter Tuning Methods for Metaheuristics

Huang, Changwu; Li, Yuanxiang; Yao, Xin

doi:10.1109/tevc.2019.2921598

Cited by 258 publications

(115 citation statements)

References 93 publications

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“…In optimization, the parameter setting is known as a strategy for providing larger flexibility and robustness to the solvers but requires an extremely careful initialization [48,49]. Indeed, parameters of algorithms influence on the efficiency of the solving process.…”

Section: Proposed Approachmentioning

confidence: 99%

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

et al. 2020

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During the last years, highly-recognized computational intelligence techniques have been proposed to treat classification problems. These automatic learning approaches lead to the most recent researches because they exhibit outstanding results. Nevertheless, to achieve this performance, artificial learning methods firstly require fine tuning of their parameters and then they need to work with the best-generated model. This process usually needs an expert user for supervising the algorithm’s performance. In this paper, we propose an optimized Extreme Learning Machine by using the Bat Algorithm, which boosts the training phase of the machine learning method to increase the accuracy, and decreasing or keeping the loss in the learning phase. To evaluate our proposal, we use the Parkinson’s Disease audio dataset taken from UCI Machine Learning Repository. Parkinson’s disease is a neurodegenerative disorder that affects over 10 million people. Although its diagnosis is through motor symptoms, it is possible to evidence the disorder through variations in the speech using machine learning techniques. Results suggest that using the bio-inspired optimization algorithm for adjusting the parameters of the Extreme Learning Machine is a real alternative for improving its performance. During the validation phase, the classification process for Parkinson’s Disease achieves a maximum accuracy of 96.74% and a minimum loss of 3.27%.

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Section: Proposed Approachmentioning

confidence: 99%

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

et al. 2020

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“…Recently, a great interest has been given to using some artificial tools in optimization. Metaheuristics play a great role in either real-life simulation or invoking intelligent learned procedures [15][16][17][18][19][20][21][22][23][24]. Metaheuristics exhibit good degrees of robustness for a wide range of problems.…”

Section: Introductionmentioning

confidence: 99%

Simulation-Based EDAs for Stochastic Programming Problems

2020

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With the rapid growth of simulation software packages, generating practical tools for simulation-based optimization has attracted a lot of interest over the last decades. In this paper, a modified method of Estimation of Distribution Algorithms (EDAs) is constructed by a combination with variable-sample techniques to deal with simulation-based optimization problems. Moreover, a new variable-sample technique is introduced to support the search process whenever the sample sizes are small, especially in the beginning of the search process. The proposed method shows efficient results by simulating several numerical experiments.

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“…This task, called algorithm configuration or parameter tuning, was traditionally performed by hand: parameter values were updated manually and the performance of each configuration assessed, allowing the user to determine which parameter settings performed best. In recent years there has been an increase in popularity of automated algorithm configurators [13].…”

Section: Introductionmentioning

confidence: 99%

Fast Perturbative Algorithm Configurators

Hall

Oliveto

Sudholt

2020

Parallel Problem Solving From Nature – PPSN XVI

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Recent work has shown that the ParamRLS and ParamILS algorithm configurators can tune some simple randomised search heuristics for standard benchmark functions in linear expected time in the size of the parameter space. In this paper we prove a linear lower bound on the expected time to optimise any parameter tuning problem for ParamRLS, ParamILS as well as for larger classes of algorithm configurators. We propose a harmonic mutation operator for perturbative algorithm configurators that provably tunes single-parameter algorithms in polylogarithmic time for unimodal and approximately unimodal (i.e., non-smooth, rugged with an underlying gradient towards the optimum) parameter spaces. It is suitable as a general-purpose operator since even on worst-case (e.g., deceptive) landscapes it is only by at most a logarithmic factor slower than the default ones used by ParamRLS and ParamILS. An experimental analysis confirms the superiority of the approach in practice for a number of configuration scenarios, including ones involving more than one parameter.

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A Survey of Automatic Parameter Tuning Methods for Metaheuristics

Cited by 258 publications

References 93 publications

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

An Optimized Brain-Based Algorithm for Classifying Parkinson’s Disease

Simulation-Based EDAs for Stochastic Programming Problems

Fast Perturbative Algorithm Configurators

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