Sampled Walk and Binary Fitness Landscapes Exploration

Tari, Sara; Basseur, Matthieu; Goëffon, Adrien

doi:10.1007/978-3-319-78133-4_4

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…When this factor is increased by a small gap, important variable contribute more to the ruggedness. Interestingly, while ruggedness level is low, it seems higher around important variables, possibly indicating these landscapes are globally smooth but locally rugged, as some UBQP landscapes [21]. Except for the subproblems C i , PUBO i parameters strongly impact the neutrality level of important variables.…”

Section: Resultsmentioning

confidence: 99%

PUBO$$_i$$: A Tunable Benchmark with Variable Importance

Tari

Vérel

Omidvar

2022

Evolutionary Computation in Combinatorial Optimization

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In this work, we present the benchmark generator PUBOi, Polynomial Unconstrained Binary Optimization, that combines subproblems to create instances of pseudo-boolean optimization problems. Any mono-objective pseudoboolean functions including existing classical optimization problems can be expressed with Walsh functions. The benchmark generator can tune main features of problems such as problem dimension, non-linearity degree, and neutrality. Additionally, to be able to create instances with properties similar to those of real-like combinatorial optimization problems, the goal of PUBOi is to introduce the notion of variable importance. Indeed, the importance of decision variables can be tuned using three benchmark parameters. In the version presented here, we consider four subproblems already used in Chook generator for benchmarking quantum computers and algorithms as a basis. We also present the impact of benchmark parameters using a fitness landscape analysis that empirically shows these parameters to significantly impact the variable importance.

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Section: Resultsmentioning

confidence: 99%

PUBO$$_i$$: A Tunable Benchmark with Variable Importance

Tari

Vérel

Omidvar

2022

Evolutionary Computation in Combinatorial Optimization

Self Cite

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“…Landscape analysis of optimisation problems provides a mechanism for explaining algorithm behaviour and identifying classes of problems that are suited to particular algorithms. Studies that describe the use of landscape analysis for understanding algorithm behaviour include: explaining evolutionary algorithm behaviour in dynamic environments [8] and the dynamics in coevolutionary games [12]; understanding the behaviour of local search algorithms [105,106]; explaining performance differences between search-andscore algorithms for learning Bayesian network structures [107]; explaining the effect of different mutation operators [108] and different function sets [109] in genetic programming; explaining the performance of different real-valued evolutionary algorithms [87]; explaining the performance of multiobjective evolutionary algorithms [52,110]; understanding evolvability in grammatical evolution [111]; understanding the effect of funnels in the landscape on metaheuristic performance [112]; and explaining the performance of evolutionary algorithms in generating unit tests for software [93].…”

Section: Understanding and Explaining Algorithm Behaviourmentioning

confidence: 99%

A Survey of Advances in Landscape Analysis for Optimisation

Malan

2021

Algorithms

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Fitness landscapes were proposed in 1932 as an abstract notion for understanding biological evolution and were later used to explain evolutionary algorithm behaviour. The last ten years has seen the field of fitness landscape analysis develop from a largely theoretical idea in evolutionary computation to a practical tool applied in optimisation in general and more recently in machine learning. With this widened scope, new types of landscapes have emerged such as multiobjective landscapes, violation landscapes, dynamic and coupled landscapes and error landscapes. This survey is a follow-up from a 2013 survey on fitness landscapes and includes an additional 11 landscape analysis techniques. The paper also includes a survey on the applications of landscape analysis for understanding complex problems and explaining algorithm behaviour, as well as algorithm performance prediction and automated algorithm configuration and selection. The extensive use of landscape analysis in a broad range of areas highlights the wide applicability of the techniques and the paper discusses some opportunities for further research in this growing field.

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“…The autocorrelation of fitness introduced by Weinberger [25] defines the correlation of fitness values during a random walk. The correlation value and the autocorrelation length give a measure of the ruggedness of the landscape [26]. Besides, the accuracy of the autocorrelation coefficients is defined by 1 2 √ , where is the length of the random walk [27].…”

Section: Fitness Landscapementioning

confidence: 99%

Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

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Fonlupt

Vérel

et al. 2019

Applied Soft Computing

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Finding optimal traffic light timings at road intersections is a mandatory step for urban planners wishing to achieve a sustainable mobility in modern cities. Increasing congestion situations constantly require urbanists to enhance traffic fluidity, while limiting pollutant emissions and vehicle consumption to improve inhabitants' welfare. Various mono or multi-objective optimization methods, such as evolutionary algorithms, fuzzy logic algorithms or even particle swarm optimizations, help to reach optimal traffic signal settings. However, those methods are usually designed to tackle very specific transportation configurations. Here, we introduce an extended version of the sialac benchmark, bringing together several real-world-like study cases with various features related to population, working activities, or traffic light devices. We drive a fitness landscape analysis on these various benchmark instances, which helps to improve the design of optimization algorithms for this class of real-world mobility problems. Thereby, we propose a new adaptive optimization algorithm to tackle each scenario of the benchmark.

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Sampled Walk and Binary Fitness Landscapes Exploration

Cited by 7 publications

References 13 publications

PUBO$$_i$$: A Tunable Benchmark with Variable Importance

PUBO$$_i$$: A Tunable Benchmark with Variable Importance

A Survey of Advances in Landscape Analysis for Optimisation

Fitness landscapes analysis and adaptive algorithms design for traffic lights optimization on SIALAC benchmark

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