A comparison of predictive measures of problem difficulty in evolutionary algorithms

Naudts, Bart; Kallel, Leila

doi:10.1109/4235.843491

Cited by 136 publications

(83 citation statements)

References 19 publications

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“…Other works have also shown some weaknesses in fdc [5,49]. Both [60] and [41] construct examples which demonstrate that the fdc can be "blinded" by particular qualities of the search space, and that it can be misleading. There is, however, a vast amount of work where Jones' approach has been successfully used in a wide variety of problems [12,16,44,45].…”

Section: Fitness Distance Correlationmentioning

confidence: 99%

“…Several other approaches to studying landscapes and problem difficulty have also been proposed, generally in a non-GP context, including: other measures of landscape correlation [68], autocorrelation [39]; epistasis, which measures the degree of interaction between genes and is a component of deception [20,21,41]; monotonicity, which is similar to fdc in that it measures how often fitness improves despite distance to the optimum increasing [41]; and distance distortion which relates overall distance in the genotype and phenotype spaces [52]. All of these measures are to some extent related.…”

Section: Other Landscape Measuresmentioning

confidence: 99%

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Defining locality as a problem difficulty measure in genetic programming

Galván

McDermott

O’Neill

et al. 2011

Genet Program Evolvable Mach

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Publication information Genetic Programming and Evolvable Machines, 12 (4): 365-401Publisher Springer Abstract A mapping is local if it preserves neighbourhood. In Evolutionary Computation, locality is generally described as the property that neighbouring genotypes correspond to neighbouring phenotypes. A representation has high locality if most genotypic neighbours are mapped to phenotypic neighbours. Locality is seen as a key element in performing effective evolutionary search. It is believed that a representation that has high locality will perform better in evolutionary search and the contrary is true for a representation that has low locality. When locality was introduced, it was the genotype-phenotype mapping in bitstring-based Genetic Algorithms which was of interest; more recently, it has also been used to study the same mapping in Grammatical Evolution. To our knowledge, there are few explicit studies of locality in Genetic Programming (GP). The goal of this paper is to shed some light on locality in GP and use it as an indicator of problem difficulty. Strictly speaking, in GP the genotype and the phenotype are not distinct. We attempt to extend the standard quantitative definition of genotype-phenotype locality to the genotype-fitness mapping by considering three possible definitions. We consider the effects of these definitions in both continuous-and discrete-valued fitness functions. We compare three different GP representations (two of them induced by using different function sets and the other using a slightly different GP encoding) and six different mutation operators. Results indicate that one definition of locality is better in predicting performance.

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Section: Fitness Distance Correlationmentioning

confidence: 99%

Section: Other Landscape Measuresmentioning

confidence: 99%

Defining locality as a problem difficulty measure in genetic programming

Galván

McDermott

O’Neill

et al. 2011

Genet Program Evolvable Mach

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“…For example, on a given problem, a GA coupled with a local search may find the optimum every time, regardless of the initial population, while the same GA without the local search operator may find the problem very difficult. Studies that attempt to demonstrate the unreliability of fitness function statistics (including Kallel et al, 1999;Kinnear Jr, 1994;Naudts and Kallel, 2000;Quick et al, 1998;Reeves and Wright, 1995;Rochet et al, 1998) have generally drawn conclusions about the convergence of the algorithm, without consideration of the calibration of the algorithm, or the type of algorithm.…”

Section: Fitness Evolvabilitymentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

521

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…Once a measure of hardness and the way to compute it have been chosen, the problem remains of finding a means to validate the prediction of the measure with respect to the problem instance and the algorithm. The easiest way is to use a performance measure [13]. For the purposes of the present work, performance is defined as being the proportion of the runs for which the global optimum has been found in less than 500 generations over 100 runs.…”

Section: Limitations Of the Original Definitionmentioning

confidence: 99%

Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes

Vanneschi

Tomassini

Collard

et al. 2006

Lecture Notes in Computer Science

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Abstract. Negative slope coefficient has been recently introduced and empirically proven a suitable hardness indicator for some well known genetic programming benchmarks, such as the even parity problem, the binomial-3 and the artificial ant on the Santa Fe trail. Nevertheless, the original definition of this measure contains several limitations. This paper points out some of those limitations, presents a new and more relevant definition of the negative slope coefficient and empirically shows the suitability of this new definition as a hardness measure for some genetic programming benchmarks, including the multiplexer, the intertwined spirals problem and the royal trees.

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A comparison of predictive measures of problem difficulty in evolutionary algorithms

Cited by 136 publications

References 19 publications

Defining locality as a problem difficulty measure in genetic programming

Defining locality as a problem difficulty measure in genetic programming

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Negative Slope Coefficient: A Measure to Characterize Genetic Programming Fitness Landscapes

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