Racing based approach for Metaheuristics parameter tuning

Pereira, Ivo; Madureira, Ana

doi:10.1109/cisti.2015.7170351

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Other approaches to parameter tuning include model-based methods like Sequential Parameter Optimization (SPO) proposed by [24] and racing algorithms [25,26]: they generate a population of possible configurations based on a particular distribution; members of this population are then tested and possibly discarded as soon as a statistical test shows that there is at least another individual which outclasses them; these operations are repeated until a set of good configurations is obtained. A recent trend approaches parameter tuning as a two-level optimization problem [27,28].…”

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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