1,949 research outputs found
Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms
The problem of parameterization is often central to the effective deployment
of nature-inspired algorithms. However, finding the optimal set of parameter
values for a combination of problem instance and solution method is highly
challenging, and few concrete guidelines exist on how and when such tuning may
be performed. Previous work tends to either focus on a specific algorithm or
use benchmark problems, and both of these restrictions limit the applicability
of any findings. Here, we examine a number of different algorithms, and study
them in a "problem agnostic" fashion (i.e., one that is not tied to specific
instances) by considering their performance on fitness landscapes with varying
characteristics. Using this approach, we make a number of observations on which
algorithms may (or may not) benefit from tuning, and in which specific
circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial
Life (ECAL) 2013, Taormina, Ital
Application of evolutionary rietveld method based XRD phase analysis and a self-configuring genetic algorithm to the inspection of electrolyte composition in aluminum electrolysis baths
The technological inspection of the electrolyte composition in aluminum production is performed using calibration X-ray quantitative phase analysis (QPA). For this purpose, the use of QPA by the Rietveld method, which does not require the creation of multiphase reference samples and is able to take into account the actual structure of the phases in the samples, could be promising. However, its limitations are in its low automation and in the problem of setting the correct initial values of profile and structural parameters. A possible solution to this problem is the application of the genetic algorithm we proposed earlier for finding suitable initial parameter values individually for each sample. However, the genetic algorithm also needs tuning. A self-configuring genetic algorithm that does not require tuning and provides a fully automatic analysis of the electrolyte composition by the Rietveld method was proposed, and successful testing results were presented. © 2018 by the authors. Licensee MDPI, Basel, Switzerland
Robust evolutionary algorithms
Evolutionary Algorithms (EAs) have shown great potential to solve complex real world problems, but their dependence on problem specific configuration in order to obtain high quality performance prevents EAs from achieving widespread use. While it is widely accepted that statically configuring an EA is already a complex problem, dynamic configuration of an EA is a combinatorially harder problem. Evidence provided here supports the claim that EAs achieve the best results when using dynamic configurations. By designing methods that automatically configure parts of an EA or by changing how EAs work to avoid configurable aspects, EAs can be made more robust, allowing them better performance on a wider variety of problems with less requirements on the user.
Two methods are presented in this thesis to increase the robustness of EAs. The first is a novel algorithm designed to automatically configure and dynamically update the recombination method which is used by the EA to exploit known information to create new solutions. The techniques used by this algorithm can likely be applied to other aspects of an EA in the future, leading to even more robust EAs. The second is an existing set of algorithms which only require a single configurable parameter. The analysis of the existing set led to the creation of a new variation, as well as a better understanding of how these algorithms work. Both methods are able to outperform more traditional EAs while also making both easier to apply to new problems. By building upon these methods, and perhaps combining them, EAs can become even more robust and become more widely used --Abstract, page iv
Predicting Good Configurations for GitHub and Stack Overflow Topic Models
Software repositories contain large amounts of textual data, ranging from
source code comments and issue descriptions to questions, answers, and comments
on Stack Overflow. To make sense of this textual data, topic modelling is
frequently used as a text-mining tool for the discovery of hidden semantic
structures in text bodies. Latent Dirichlet allocation (LDA) is a commonly used
topic model that aims to explain the structure of a corpus by grouping texts.
LDA requires multiple parameters to work well, and there are only rough and
sometimes conflicting guidelines available on how these parameters should be
set. In this paper, we contribute (i) a broad study of parameters to arrive at
good local optima for GitHub and Stack Overflow text corpora, (ii) an
a-posteriori characterisation of text corpora related to eight programming
languages, and (iii) an analysis of corpus feature importance via per-corpus
LDA configuration. We find that (1) popular rules of thumb for topic modelling
parameter configuration are not applicable to the corpora used in our
experiments, (2) corpora sampled from GitHub and Stack Overflow have different
characteristics and require different configurations to achieve good model fit,
and (3) we can predict good configurations for unseen corpora reliably. These
findings support researchers and practitioners in efficiently determining
suitable configurations for topic modelling when analysing textual data
contained in software repositories.Comment: to appear as full paper at MSR 2019, the 16th International
Conference on Mining Software Repositorie
An evolutionary algorithm for online, resource constrained, multi-vehicle sensing mission planning
Mobile robotic platforms are an indispensable tool for various scientific and
industrial applications. Robots are used to undertake missions whose execution
is constrained by various factors, such as the allocated time or their
remaining energy. Existing solutions for resource constrained multi-robot
sensing mission planning provide optimal plans at a prohibitive computational
complexity for online application [1],[2],[3]. A heuristic approach exists for
an online, resource constrained sensing mission planning for a single vehicle
[4]. This work proposes a Genetic Algorithm (GA) based heuristic for the
Correlated Team Orienteering Problem (CTOP) that is used for planning sensing
and monitoring missions for robotic teams that operate under resource
constraints. The heuristic is compared against optimal Mixed Integer Quadratic
Programming (MIQP) solutions. Results show that the quality of the heuristic
solution is at the worst case equal to the 5% optimal solution. The heuristic
solution proves to be at least 300 times more time efficient in the worst
tested case. The GA heuristic execution required in the worst case less than a
second making it suitable for online execution.Comment: 8 pages, 5 figures, accepted for publication in Robotics and
Automation Letters (RA-L
What can we learn from multi-objective meta-optimization of Evolutionary Algorithms in continuous domains?
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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