Tuners review: How crucial are set-up values to find effective parameter values?

Montero, Elizabeth; Riff, María Cristina; Rojas-Morales, Nicolás

doi:10.1016/j.engappai.2018.09.001

Cited by 15 publications

(6 citation statements)

References 11 publications

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“…The importance of parameter tuning has been frequently addressed in the last years, not only in theoretical or review papers such as [12] but also in papers with extensive experimental evidence which provide a critical assessment of such methods. In [13], while recognizing the importance of finding a good set of parameters, the authors even suggest that using approaches to algorithm tuning that are computationally demanding may be almost useless, since a relatively limited random search in the algorithm parameter space can often offer good results.…”

Section: Related Workmentioning

confidence: 99%

What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

Ugolotti¹,

Sani

Cagnoni

2019

Mathematics

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Properly configuring Evolutionary Algorithms (EAs) is a challenging task made difficult by many different details that affect EAs’ performance, such as the properties of the fitness function, time and computational constraints, and many others. EAs’ meta-optimization methods, in which a metaheuristic is used to tune the parameters of another (lower-level) metaheuristic which optimizes a given target function, most often rely on the optimization of a single property of the lower-level method. In this paper, we show that by using a multi-objective genetic algorithm to tune an EA, it is possible not only to find good parameter sets considering more objectives at the same time but also to derive generalizable results which can provide guidelines for designing EA-based applications. In particular, we present a general framework for multi-objective meta-optimization, to show that “going multi-objective” allows one to generate configurations that, besides optimally fitting an EA to a given problem, also perform well on previously unseen ones.

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Section: Related Workmentioning

confidence: 99%

What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

Ugolotti¹,

Sani

Cagnoni

2019

Mathematics

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“…However, the degree of impact on the solution has not been thoroughly studied. Hence, the GA parameters should be optimized to find the ideal simulation parameters using tuning algorithms (Eiben and Smit, 2012; Montero et al , 2018). For example, Ooi et al (2019) proposed a self-tune linear adaptive GA which modifies the mutation probability rate and the population size based on the diversity of the population.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

Cio

Vadean

et al. 2020

AIEDAM

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The development of autonomous greenhouses has caught the interest of many researchers and industrial considering their potential of offering an optimal environment for the growth of high-quality crops with minimum resources. Since an autonomous greenhouse is a mechatronic system, the consideration of its subsystem (e.g. heating systems) and component (e.g. actuators and sensors) interactions early in the design phase can ease the product development process. Indeed, this consideration could shorten the design process, reduce the number of redesign loops, and improve the performance of the overall mechatronic system. In the case of a greenhouse, it would lead to a higher quality of the crops and a better management of resources. In this work, the layout design of a general autonomous greenhouse is translated into an optimization problem statement while considering product-related dependencies. Then, a genetic algorithm is used to carry out the optimization of the layout design. The methodology is applied to the design of a fully autonomous greenhouse (45 cm × 30 cm × 30 cm) for the growth of plants in space. Although some objectives are conflictual, the developed algorithm proposes a compromise to obtain a near-optimal feasible layout design. The algorithm was also able to optimize the volume of components (occupied space) while considering the energy consumption and the overall mass. Their respective summed values are 2844.32 cm3, which represents 7% of the total volume, 5.86 W, and 655.8 g.

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“…In common, the performance of algorithm configuration methods depends on the definition of parameter search space [131], but Evoca [132] (which is a meta evolutionary algorithm) has demonstrated that it is not sensitive to the parameter's search space definition. Montero and Riff [110] suggested an efficient collaborative approach that associates the tuning process with a parameter search space definition process and so combines Evoca with I-Race and ParamILS.…”

Section: Studies On Different Aspects Of the Algorithm Configuration ...mentioning

confidence: 99%

A Literature Survey on Offline Automatic Algorithm Configuration

Eryoldaş

Durmuşoğlu

2022

Applied Sciences

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Metaheuristic and heuristic methods have many tunable parameters, and choosing their values can increase their ability to deal with hard optimization problems. Automated approaches for finding good parameter settings have attracted significant research and development efforts in the last few years. Because parameter tuning became commonly utilized in industry and research and there is a significant advancement in this area, a comprehensive review is an important requirement. Although there is very wide literature about algorithm configuration problems, a detailed survey analysis has not been conducted yet to the best of our knowledge. In this paper, we will briefly explain the automatic algorithm configuration problem and then survey the automated methods developed to handle this problem. After explaining the logic of these methods, we also argued about their main advantages and disadvantages to help researchers or practitioners select the best possible method for their specific problem. Moreover, some recommendations and possible future directions for this topic are provided as a conclusion.

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Tuners review: How crucial are set-up values to find effective parameter values?

Cited by 15 publications

References 11 publications

What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

What Can We Learn from Multi-Objective Meta-Optimization of Evolutionary Algorithms in Continuous Domains?

Evolutionary layout design synthesis of an autonomous greenhouse using product-related dependencies

A Literature Survey on Offline Automatic Algorithm Configuration

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