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

Ugolotti, Roberto; Sani, Laura; Cagnoni, Stefano

doi:10.3390/math7030232

Cited by 10 publications

(8 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This initial overhead is not only aimed at letting the retrieval system achieve the best possible final performance, but also and, more importantly, at finding a “standard” setting for the GA that can both directly guarantee good performance, and, being a good “starting guess”, minimize the fine-tuning time when transferred to other benchmarks. Our expectation, finally confirmed by the results achieved, was supported by the literature on meta-optimization, which shows that the dependence of the performance of an optimizer on the parameters of a meta-optimizer is less critical than the dependence of the final outcome of the optimization on the parameters of the optimizer [ 45 ]. If this sort of “vanishing gradient” effect did not occur as the level of meta-optimization increased, i.e., if tuning a meta-optimizer was as a critical problem as tuning the optimizer, the literature on meta-optimization would not be as extended as it actually is.…”

Section: Resultssupporting

confidence: 79%

“…GAs and, more in general, EAs have often been used for parameter tuning in their most obvious role of quality function optimizers of direct solutions to complex design problems, described by many variables with unknown dependencies among their parameters. However, they have also been used as metaoptimizers to tune either general methods, applicable in different contexts (as, for example, the design of neural networks or classifiers [ 42 , 43 , 44 ]) or even to tune other EAs [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Magliani

Sani

Cagnoni

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

Most recent computer vision tasks take into account the distribution of image features to obtain more powerful models and better performance. One of the most commonly used techniques to this purpose is the diffusion algorithm, which fuses manifold data and k-Nearest Neighbors (kNN) graphs. In this paper, we describe how we optimized diffusion in an image retrieval task aimed at mobile vision applications, in order to obtain a good trade-off between computation load and performance. From a computational efficiency viewpoint, the high complexity of the exhaustive creation of a full kNN graph for a large database renders such a process unfeasible on mobile devices. From a retrieval performance viewpoint, the diffusion parameters are strongly task-dependent and affect significantly the algorithm performance. In the method we describe herein, we tackle the first issue by using approximate algorithms in building the kNN tree. The main contribution of this work is the optimization of diffusion parameters using a genetic algorithm (GA), which allows us to guarantee high retrieval performance in spite of such a simplification. The results we have obtained confirm that the global search for the optimal diffusion parameters performed by a genetic algorithm is equivalent to a massive analysis of the diffusion parameter space for which an exhaustive search would be totally unfeasible. We show that even a grid search could often be less efficient (and effective) than the GA, i.e., that the genetic algorithm most often produces better diffusion settings when equal computing resources are available to the two approaches. Our method has been tested on several publicly-available datasets: Oxford5k, ROxford5k, Paris6k, RParis6k, and Oxford105k, and compared to other mainstream approaches.

show abstract

Section: Resultssupporting

confidence: 79%

Section: Related Workmentioning

confidence: 99%

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Magliani

Sani

Cagnoni

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Meta-optimization refers to the use of a metaheuristic to tune the parameters of another metaheuristic [25]. It has been used in both, single-objective optimization problems [25] [26], and MOPs [27] [28]. A related concept within the literature is Hyper-heuristics [29], which is a further extension to the framework of metaoptimization.…”

Section: Meta-optimization and Hyper-heuristicsmentioning

confidence: 99%

PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

Cuervo¹,

Melgarejo²,

Blanco-Cañon³

et al. 2021

Preprint

View full text Add to dashboard Cite

We present an algorithm for multi-objective optimization of computationally expensive problems. The proposed algorithm is based on solving a set of surrogate problems defined by models of the real one, so that only solutions estimated to be approximately Pareto-optimal are evaluated using the real expensive functions. Aside of the search for solutions, our algorithm also performs a meta-search for optimal surrogate models and navigation strategies for the optimization landscape, therefore adapting the search strategy for solutions to the problem as new information about it is obtained. The competitiveness of our approach is demonstrated by an experimental comparison with one state-of-the-art surrogate-assisted evolutionary algorithm on a set of benchmark problems.

show abstract

“…There are also some studies use a metaheuristic optimization algorithm to optimize the parameters of DE, such as [34,35].…”

Section: Related Work On Parameter Control Strategy For Dementioning

confidence: 99%

Parameter Combination Framework for the Differential Evolution Algorithm

Zhang

Dong

2019

Algorithms

View full text Add to dashboard Cite

The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.

show abstract

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

Cited by 10 publications

References 37 publications

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision

PAMELI: A Meta-Algorithm for Computationally Expensive Multi-Objective Optimization Problems

Parameter Combination Framework for the Differential Evolution Algorithm

Contact Info

Product

Resources

About