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

Abstract

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic that uses low-level heuristic (LLH) selection methods that apply population evolvability. DPEM_HH uses dynamic multiobjective evolutionary algorithms (DMOEAs) as LLHs to solve dynamic multi-objective optimization problems (DMOPs). Population evolvability is incorporated in DPEM_HH by calculating it on each candidate DMOEA for a set of sampled generations after a change is detected, using those values to select which LLH will be applied. DPEM_HH is tested on multiple DMOPs with dynamic properties that provide several challenges. Experimental results show that DPEM_HH with a greedy LLH selection method that uses average population evolvability values can produce solutions closer to the Pareto optimal front with equal to or better diversity than previously proposed heuristics. This shows the effectiveness and feasibility of the application of population evolvability on hyperheuristics to solve dynamic optimization problems.

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

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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.…”

“…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.…”

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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.…”

“…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.…”