On Classifications of Fitness Functions

Jansen, Thomas

doi:10.1007/978-3-662-04448-3_18

Cited by 30 publications

(32 citation statements)

References 19 publications

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“…As such, several complementary measures are necessary [10,95,135]. Second, ELA methods require a large sample to be precise [66,95,149]. The sample size grows exponentially with D; hence, ELA methods are imprecise in polynomial time [53].…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned in Section 3.2, data driven methods, know as ELA methods, are the only valid approach to measure the problem characteristics for BCOPs. ELA is an umbrella term for analytical, approximated and non-predictive methods [53,66,86,91,104] originally developed for combinatorial optimization problems [137]. For BCOPs, the existing methods are adaptations from their combinatorial counterparts [19,20,85,95,101,103,144,150], or purposely built for continuous spaces [19,83,91,98,120].…”

Section: Characteristics Space: Exploratory Landscape Analysis Methodsmentioning

confidence: 99%

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Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

Muñoz

Sun

Kirley

et al. 2015

Information Sciences

174

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a b s t r a c tSelecting the most appropriate algorithm to use when attempting to solve a black-box continuous optimization problem is a challenging task. Such problems typically lack algebraic expressions, it is not possible to calculate derivative information, and the problem may exhibit uncertainty or noise. In many cases, the input and output variables are analyzed without considering the internal details of the problem. Algorithm selection requires expert knowledge of search algorithm efficacy and skills in algorithm engineering and statistics. Even with the necessary knowledge and skills, success is not guaranteed.In this paper, we present a survey of methods for algorithm selection in the black-box continuous optimization domain. We start the review by presenting Rice's (1976) selection framework. We describe each of the four component spaces -problem, algorithm, performance and characteristic -in terms of requirements for black-box continuous optimization problems. This is followed by an examination of exploratory landscape analysis methods that can be used to effectively extract the problem characteristics. Subsequently, we propose a classification of the landscape analysis methods based on their order, neighborhood structure and computational complexity. We then discuss applications of the algorithm selection framework and the relationship between it and algorithm portfolios, hybrid meta-heuristics, and hyper-heuristics. The paper concludes with the identification of key challenges and proposes future research directions.

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Section: Discussionmentioning

confidence: 99%

Section: Characteristics Space: Exploratory Landscape Analysis Methodsmentioning

confidence: 99%

Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

Muñoz

Sun

Kirley

et al. 2015

Information Sciences

174

View full text Add to dashboard Cite

show abstract

“…It has also been shown that it does not reliably predict the difficulty of optimising the problem (Altenberg, 1997;Jansen, 2001;Kallel, 1998, 2000;Quick et al, 1998;Reeves, 1999).…”

Section: Original Formulationmentioning

confidence: 99%

Characterising the searchability of continuous optimisation problems for PSO

Malan

Engelbrecht

2014

Swarm Intell

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The focus of research in swarm intelligence has been largely on the algorithmic side with relatively little attention being paid to the study of problems and the behaviour of algorithms in relation to problems. When a new algorithm or variation on an existing algorithm is proposed in the literature, there is seldom any discussion or analysis of algorithm weaknesses and on what kinds of problems the algorithm is expected to fail. Fitness landscape analysis is an approach that can be used to analyse optimisation problems. By characterising problems in terms of fitness landscape features, the link between problem types and algorithm performance can be studied. This article investigates a number of measures for analysing the ability of a search process to improve fitness on a particular problem (called evolvability in literature, but referred to as searchability in this study to broaden the scope to non-evolutionary based search techniques). A number of existing fitness landscape analysis techniques originally proposed for discrete problems are adapted to work in continuous search spaces. For a range of benchmark problems, the proposed searchability measures are viewed alongside performance measures for a traditional global best particle swarm optimisation (PSO) algorithm. Empirical results show that no single measure can be used as a predictor of PSO performance, but that multiple measures of different fitness landscape features can be used together to predict PSO failure.

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“…The most prominent of these may be the fitness-distance correlation (FDC) [46]. However, further theoretical investigations of Jansen [44] and He et al [38] have largely found the existing measures unsuitable for predictive purposes, so watching out for new properties surely makes sense. Furthermore, in exploratory landscape analysis, one is especially interested in what can be achieved with only few evaluations of the problem, as the ultimate goal usually is to set up a good optimization algorithm for expensive problems with unknown properties.…”

Section: Important Problem Propertiesmentioning

confidence: 99%

Experimental Analysis of Optimization Algorithms: Tuning and Beyond

Bartz-Beielstein

Preuß

2013

Natural Computing Series

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This chapter comprises the essence of several years of tutorials the authors gave on experimental research in evolutionary computation. We highlight the renaissance of experimental techniques also in other fields to especially focus on the specific conditions of experimental research in computer science, or more concrete, metaheuristic optimization. The experimental setup is discussed together with the pitfalls awaiting the unexperienced (and sometimes even the experienced). We present a severity criterion as a meta-statistical concept for evaluating statistical inferences, which can be used to avoid fallacies, i.e., misconceptions resulting from incorrect reasoning in argumentation caused by floor or ceiling effects. The sequential parameter optimization is discussed as a meta-statistical framework which integrates concepts such as severity. Parameter tuning is considered as a relatively new tool in method design and analysis, and it leads to the question of adaptability of optimization algorithms. Another branch of experimentation aims for attaining more concrete problem knowledge, we may term it 'exploratory landscape analysis', containing sample and visualization techniques that are often applied but not seen as being a methodological contribution. However, this chapter is not only a renarration of well known facts. We also try a look into the future to estimate what the hot topics of methodological research will be in the next years and what changes we may expect for the whole community.

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On Classifications of Fitness Functions

Cited by 30 publications

References 19 publications

Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

Algorithm selection for black-box continuous optimization problems: A survey on methods and challenges

Characterising the searchability of continuous optimisation problems for PSO

Experimental Analysis of Optimization Algorithms: Tuning and Beyond

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