Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

Consoli, Pietro A.; Mei, Yi; Minku, Leandro L.; Yao, Xin

doi:10.1007/s00500-016-2126-x

Cited by 20 publications

(3 citation statements)

References 37 publications

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“…Landscapes have different characteristics and can reflect the internal properties of problems, so the required evolution operators are also different. Detecting the fitness landscape of a problem, the algorithm applied to it can be selected according to the landscape, so the landscape information can be considered and used to guide the design of evolution operators in CMOEAs [208].…”

Section: Future Directionsmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

162

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Handling constrained multi-objective optimization problems (CMOPs) is extremely challenging, since multiple conflicting objectives subject to various constraints require to be simultaneously optimized. To deal with CMOPs, numerous constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed in recent years, and they have achieved promising performance. However, there has been few literature on the systematic review of the related studies currently. This article provides a comprehensive survey for evolutionary constrained multi-objective optimization. We first review a large number of CMOEAs through categorization, and analyze their advantages and drawbacks in each category. Then, we summarize the benchmark test problems and investigate the performance of different constraint handling techniques and different algorithms, followed by some emerging and representative applications of CMOEAs. Finally, we discuss some new challenges and point out some directions of the future research in the field of evolutionary constrained multi-objective optimization.

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Section: Future Directionsmentioning

confidence: 99%

A Survey on Evolutionary Constrained Multiobjective Optimization

Liang

Ban

et al. 2023

IEEE Trans. Evol. Computat.

162

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“…• genetic algorithms: using the fitness distance correlation landscape measure to dynamically adjust the migration period in a distributed genetic algorithm [119], selecting a crossover operator based on fitness landscape properties [120], using fitness landscape features to estimate the optimal population size [121]; • differential evolution algorithms: adapting the strategy and adjusting the control parameters based on detected landscape modality [122,123], adapting the mutation strategy based on landscape features [124,125], algorithm configuration based on exploratory landscape features with an empirical performance model [126]; • memetic algorithms: analysis of the separability of problems to automatically select operators [45] and the use of four fitness landscape analysis techniques to inform the most suitable crossover operator [127]; • selection of CMA-ES algorithm configuration using a trained model for predicting performance based on landscape features that was shown to outperform the default setting of CMA-ES [128]; • surrogate-assisted particle swarm optimisation, where fitness landscape analysis was used to select surrogate models [129]; and • decomposition-based multiobjective evolutionary algorithms (MOEA/D), where the addition of landscape information improved the behaviour of the adaptive operator selection mechanism [130].…”

Section: Automated Algorithm Selectionmentioning

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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“…In parameter control, self-adaptive parameter control, such as step-size control and covariance matrix adaptation in covariance matrix adaptation ES (CMA-ES) [23], is the stateof-the-art method for numerical parameter control. Adaptive operator selection (AOS) is a popular method to dynamically determine which operator(s) should be applied during the run of an optimization algorithm (i.e., categorical parameter control), based on its performance history of available operators [24]- [26]. More description and achievements in parameter control could be found in [27].…”

mentioning

confidence: 99%

A Survey of Automatic Parameter Tuning Methods for Metaheuristics

Huang

Yao

2020

IEEE Trans. Evol. Computat.

Self Cite

258

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Parameter tuning, that is, to find appropriate parameter settings (or configurations) of algorithms so that their performance is optimized, is an important task in the development and application of metaheuristics. Automating this task, i.e., developing algorithmic procedure to address parameter tuning task, is highly desired and has attracted significant attention from the researchers and practitioners. During last two decades, many automatic parameter tuning approaches have been proposed. This paper presents a comprehensive survey of automatic parameter tuning methods for metaheuristics. A new classification (or taxonomy) of automatic parameter tuning methods is introduced according to the structure of tuning methods. The existing automatic parameter tuning approaches are consequently classified into three categories: 1) simple generateevaluate methods; 2) iterative generate-evaluate methods; and 3) high-level generate-evaluate methods. Then, these three categories of tuning methods are reviewed in sequence. In addition to the description of each tuning method, its main strengths and weaknesses are discussed, which is helpful for new researchers or practitioners to select appropriate tuning methods to use. Furthermore, some challenges and directions of this field are pointed out for further research.

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Dynamic selection of evolutionary operators based on online learning and fitness landscape analysis

Cited by 20 publications

References 37 publications

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey on Evolutionary Constrained Multiobjective Optimization

A Survey of Advances in Landscape Analysis for Optimisation

A Survey of Automatic Parameter Tuning Methods for Metaheuristics

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