Application of Population Evolvability in a Hyper-Heuristic for Dynamic Multi-Objective Optimization

Macias-Escobar, Teodoro; Cruz-Reyes, Laura; Dorronsoro, Bernabè; Fraire-Huacuja, Héctor J.; Rangel-Valdez, Nelson; Gómez-Santillán, Claudia

doi:10.3846/tede.2019.10291

Cited by 5 publications

(5 citation statements)

References 53 publications

(82 reference statements)

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“…On the assumption of having established a minimum fitness as a stopping criterion, if no individual reached it, the process would be repeated using the new generation as the population, so that the fitness of the population would improve in successive generations. Macias-Escobar et al (2019) showed the effectiveness of population evolvability to solve dynamic optimization problems.…”

Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Clavería

Monte

Porras

2021

Technological and Economic Development of Economy

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In this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms’ and households’ expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.

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Section: Methodsmentioning

confidence: 99%

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Clavería

Monte

Porras

2021

Technological and Economic Development of Economy

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“…Many predictors benefit from the feature selection process since it reduces overfitting and improves accuracy, among other things (Chandrashekar and Sahin 2014). In the literature (Wang et al 2017;Macias-Escobar et al 2019), fitness landscape analysis has been shown to be an effective technique for analysing the hardness of an optimisation problem by extracting its features. Here, we review some existing approaches that are most closely related to the work proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…To avoid a computationally intensive operation, the work suggests that the number of sampled generations must be carefully defined. In Macias-Escobar et al (2019), a very similar approach has been proposed to apply population evolvability in a hyperheuristic, named Dynamic Population-Evolvability based Multiobjective Hyperheuristic. In Tan et al (2021), the authors proposed a differential evolution (DE) with an adaptive mutation operator based on fitness landscape, where a random forest based on fitness landscape is implemented for an adaptive mutation operator that selects DE's mutation strategy online.…”

Section: Related Workmentioning

confidence: 99%

Feature-based search space characterisation for data-driven adaptive operator selection

Aydin,

Durgut,

Rakib

et al. 2023

Evolving Systems

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Combinatorial optimisation problems are known as unpredictable and challenging due to their nature and complexity. One way to reduce the unpredictability of such problems is to identify features and the characteristics that can be utilised to guide the search using domain-knowledge and act accordingly. Many problem solving algorithms use multiple complementary operators in patterns to handle such unpredictable cases. A well-characterised search space may help to evaluate the problem states better and select/apply a neighbourhood operator to generate more productive new problem states that allow for a smoother path to the final/optimum solutions. This applies to the algorithms that use multiple operators to solve problems. However, the remaining challenge is determining how to select an operator in an optimal way from the set of operators while taking the search space conditions into consideration. Recent research shows the success of adaptive operator selection to address this problem. However, efficiency and scalability issues persist in this regard. In addition, selecting the most representative features remains crucial in addressing problem complexity and inducing commonality for transferring experience across domains. This paper investigates if a problem can be represented by a number of features identified by landscape analysis, and whether an adaptive operator selection scheme can be constructed using Machine Learning (ML) techniques to address the efficiency and scalability problem. The proposed method determines the optimal categorisation by analysing the predictivity of a set of features using the most well-known supervised ML techniques. The identified set of features is then used to construct an adaptive operator selection scheme. The findings of the experiments demonstrate that supervised ML algorithms are highly effective when building adaptable operator selectors.

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“…Many predictors benefit from the feature selection process since it reduces overfitting and improves accuracy, among other things [2]. In the literature [23,12], fitness landscape analysis has been shown to be an effective technique for analysing the hardness of an optimization problem by extracting its features. Here, we review some existing approaches that are most closely related to the work proposed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…To avoid a computationally intensive operation, the work suggests that the number of sampled generations must be carefully defined. In [12], a very similar approach has been proposed to apply population evolvability in a hyper-heuristic, named Dynamic Population-Evolvability based Multi-objective Hyper-heuristic. In [21], the authors proposed a differential evolution (DE) with an adaptive mutation operator based on fitness landscape, where a random forest based on fitness landscape is implemented for an adaptive mutation operator that selects DE's mutation strategy online.…”

Section: Related Workmentioning

confidence: 99%

Analysing the Predictivity of Features to Characterise the Search Space

Durgut

Aydın

Ihshaish

et al. 2022

Lecture Notes in Computer Science

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Exploring search spaces is one of the most unpredictable challenges that has attracted the interest of researchers for decades. One way to handle unpredictability is to characterise the search spaces and take actions accordingly. A well-characterised search space can assist in mapping the problem states to a set of operators for generating new problem states. In this paper, a landscape analysis-based set of features has been analysed using the most renown machine learning approaches to determine the optimal feature set. However, in order to deal with problem complexity and induce commonality for transferring experience across domains, the selection of the most representative features remains crucial. The proposed approach analyses the predictivity of a set of features in order to determine the best categorization.

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Application of Population Evolvability in a Hyper-Heuristic for Dynamic Multi-Objective Optimization

Cited by 5 publications

References 53 publications

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

A Genetic Programming Approach for Estimating Economic Sentiment in the Baltic Countries and the European Union

Feature-based search space characterisation for data-driven adaptive operator selection

Analysing the Predictivity of Features to Characterise the Search Space

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