Where are bottlenecks in NK fitness landscapes?

Vérel, Sebástien; Collard, Philippe; Clergue, Manuel

doi:10.1109/cec.2003.1299585

Cited by 32 publications

(25 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Three such techniques include fitness evolvability portraits (Smith et al, 2002), fitness clouds (Verel et al, 2003) and fitness-probability clouds (Lu et al, 2011). Although visual plots are potentially useful for human analysis, numerical output is more useful for facilitating automated analysis of problem features for performance prediction.…”

Section: Evolvability / Searchabilitymentioning

confidence: 99%

“…This section describes fitness distance correlation (Jones and Forrest, 1995), information landscape hardness (Borenstein and Poli, 2005b), fitness clouds (Verel et al, 2003) and the negative slope coefficient , and proposes ways of adapting these techniques to measure searchability of continuous problems in the context of PSO algorithms.…”

Section: Evolvability / Searchabilitymentioning

confidence: 99%

See 1 more Smart Citation

Characterising the searchability of continuous optimisation problems for PSO

Malan

Engelbrecht

2014

Swarm Intell

View full text Add to dashboard Cite

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.

show abstract

Section: Evolvability / Searchabilitymentioning

confidence: 99%

Section: Evolvability / Searchabilitymentioning

confidence: 99%

Characterising the searchability of continuous optimisation problems for PSO

Malan

Engelbrecht

2014

Swarm Intell

View full text Add to dashboard Cite

show abstract

“…The most natural way to study evolvability is, probably, to plot the fitness values of individuals against the fitness values of their neighbors, where a neighbor is obtained by applying one step of a genetic operator to the individual. Such a plot has been first introduced for binary landscapes by Vérel and coworkers [21] and called by them fitness cloud. In this paper, the genetic operator used to generate fitness clouds is standard subtree mutation [8], i.e.…”

Section: Fitness Cloudsmentioning

confidence: 99%

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

Vanneschi

Tomassini

Collard

et al. 2006

Lecture Notes in Computer Science

View full text Add to dashboard Cite

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.

show abstract

“…However, autocorrelation is not relevant to study precisely the ruggedness of one particular fitness landscape, as the scale of the correlation value is not easily interpretable alone without any comparison. In this work, we prefer to use the fitness cloud which gives a better insight into the correlation of fitness between neighboring solutions [27]. The fitness cloud is the conditional bivariate probability density of reaching a fitness value from a solution of a given fitness value applying a local search operator [7].…”

Section: Multi-modality and Ruggednessmentioning

confidence: 99%

An Iterated Local Search to find many solutions of the 6-states Firing Squad Synchronization Problem

Clergue

Vérel

Formenti

2018

Applied Soft Computing

View full text Add to dashboard Cite

This paper proposes an optimization approach for solving a classical problem in cellular automata theory: the 6-states Firing Squad Synchronization Problem (FSSP). To this purpose, we introduce an original optimization function which quantifies the quality of solutions according only to the main goal of the problem without taking into account any side information about cellular automata computations. This function is used for a dedicated Iterated Local Search algorithm which finds several hundreds of new solutions of the FSSP. Note, that up to present only one human-designed solution was known which is optimal in time. Most of the new solutions found by our algorithm have lower complexity (in terms of number of transitions rules used). An analysis of the fitness landscape for FSSP explains why, counter-intuitively, local search strategy can achieve good results for FSSP.

show abstract

Where are bottlenecks in NK fitness landscapes?

Cited by 32 publications

References 10 publications

Characterising the searchability of continuous optimisation problems for PSO

Characterising the searchability of continuous optimisation problems for PSO

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

An Iterated Local Search to find many solutions of the 6-states Firing Squad Synchronization Problem

Contact Info

Product

Resources

About