Automated Algorithm Configuration and Parameter Tuning

Hoos, Holger H.

doi:10.1007/978-3-642-21434-9_3

Cited by 112 publications

(89 citation statements)

References 51 publications

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“…When ran each of these Pareto optimal solutions on the test set, one failed to complete execution and another was dominated by other solutions in the set thus leaving 12 Pareto optimal solutions and the original, unaltered program which was also found to be Pareto optimal when run on the test set 6 . These are shown in Figure 1 (the original program included as the left-most solution).…”

Section: Resultsmentioning

confidence: 99%

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Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV

Bruce¹,

Aitken

Petke³

2016

Search Based Software Engineering

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Abstract. OpenCV is a commonly used computer vision library containing a wide variety of algorithms for the AI community. This paper uses deep parameter optimisation to investigate improvements to face detection using the Viola-Jones algorithm in OpenCV, allowing a tradeoff between execution time and classification accuracy. Our results show that execution time can be decreased by 48% if a 1.80% classification inaccuracy is permitted (compared to 1.04% classification inaccuracy of the original, unmodified algorithm). Further execution time savings are possible depending on the degree of inaccuracy deemed acceptable by the user.

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Section: Resultsmentioning

confidence: 99%

“…Deep parameter optimisation [14] is a technique that delves deeper into parameters that can affect non-functional program properties than traditional approaches (e.g., used in the machine learning community [6]). This forms a larger search-space opening new routes over which optimisation can be performed.…”

Section: Deep Parameter Optimisationmentioning

confidence: 99%

Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV

Bruce¹,

Aitken

Petke³

2016

Search Based Software Engineering

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“…Small values of s (i.e. s = 2) have been found to be sufficient for obtaining good performance of the overall configuration procedure (Hoos, 2012). In Hutter et al (Hutter et al, 2009), extensive evidence is presented that ParamILS can find substantially improved parameter configurations of complex and highly optimized algorithms.…”

Section: Automated Algorithm Configuration (Offline Tuning)mentioning

confidence: 99%

Effective learning hyper-heuristics for the course timetabling problem

Soria-Alcaraz

Ochoa

Swan

et al. 2014

European Journal of Operational Research

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Course timetabling is an important and recurring administrative activity in most educational institutions. This article combines a general modeling methodology with effective learning hyper-heuristics to solve this problem. The proposed hyper-heuristics are based on an iterated local search procedure that autonomously combines a set of move operators. Two types of learning for operator selection are contrasted: a static (offline) approach, with a clear distinction between training and execution phases; and a dynamic approach that learns on the fly. The resulting algorithms are tested over the set of real-world instances collected by the first and second International Timetabling competitions. The dynamic scheme statistically outperforms the static counterpart, and produces competitive results when compared to the state-of-the-art, even producing a new best-known solution. Importantly, our study illustrates that algorithms with increased autonomy and generality can outperform human designed problem-specific algorithms.

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“…In recent years, a number of automatic configuration methods have been developed and recent overviews are available in the literature [2,6,8,9]. The method proposed above to automatically improve the anytime behaviour of MOEAs is mostly independent of the automatic configuration method used.…”

Section: Anytime Optimizationmentioning

confidence: 99%

Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms

Radulescu

López-Ibáñez

Stützle

2013

Lecture Notes in Computer Science

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Abstract. An algorithm that returns as low-cost solutions as possible at any moment of its execution is said to have a good anytime behaviour. The problem of optimising anytime behaviour can be modelled as a biobjective non-dominated front, where the goal is to minimise both time and cost. Using a unary quality measure such as the hypervolume indicator, the analysis of the anytime behaviour can be converted into a single-objective problem. In this manner, available automatic configuration tools can be applied to improve the anytime behaviour of an algorithm. If we want to optimise the anytime behaviour of multi-objective algorithms, we may apply again unary quality measures to obtain a scalar value for measuring the obtained approximation to the Pareto front. Thus, for multi-objective algorithms, the anytime behaviour may be described in terms of the curve of the hypervolume over time, and the quality of this bi-objective tradeoff curve be evaluated according to its hypervolume. Using this approach, we can automatically improve the anytime behaviour of multi-objective evolutionary algorithms (MOEAs). In this article, we first introduce this approach and then experimentally study the improvements obtained considering three MOEAs, namely, IBEA, NSGA-II and SPEA2.

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Automated Algorithm Configuration and Parameter Tuning

Cited by 112 publications

References 51 publications

Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV

Deep Parameter Optimisation for Face Detection Using the Viola-Jones Algorithm in OpenCV

Effective learning hyper-heuristics for the course timetabling problem

Automatically Improving the Anytime Behaviour of Multiobjective Evolutionary Algorithms

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