Surrogate-Assisted Fitness Landscape Analysis for Computationally Expensive Optimization

Werth, Bernhard; Pitzer, Erik; Affenzeller, Michael

doi:10.1007/978-3-030-45093-9_30

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The proposed algorithms can be applied to many real-world applications involving expensive simulations, including box-type boom designing problem [58], material flow optimization problems [59], algorithm selection problems [60], and hyperparameter tuning problems in the machine learning field, just to name a few. For future work, it is worthwhile to study the multi-objective acquisition functions that consider the correlations among each coordinate using the multioutput Gaussian process, as all of the existing multi-objective acquisition functions simply assume the independence among each objective.…”

Section: Discussionmentioning

confidence: 99%

A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement

Yang¹,

Dong²,

Affenzeller³

2022

Preprint

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Bayesian global optimization (BGO) is an efficient surrogate-assisted technique for problems involving expensive evaluations. A parallel technique can be used to parallelly evaluate the true-expensive objective functions in one iteration to boost the execution time. An effective and straightforward approach is to design an acquisition function that can evaluate the performance of a bath of multiple solutions, instead of a single point/solution, in one iteration. This paper proposes five alternatives of Probability of Improvement (PoI) with multiple points in a batch (q-PoI) for multi-objective Bayesian global optimization (MOBGO), taking the covariance among multiple points into account. Both exact computational formulas and the Monte Carlo approximation algorithms for all proposed q-PoIs are provided. Based on the distribution of the multiple points relevant to the Pareto-front, the position-dependent behavior of the five q-PoIs is investigated. Moreover, the five q-PoIs are compared with the other nine state-of-the-art and recently proposed batch MOBGO algorithms on twenty bio-objective benchmarks. The empirical experiments on different variety of benchmarks are conducted to demonstrate the effectiveness of two greedy q-PoIs (q-PoI best and q-PoI all ) on lowdimensional problems and the effectiveness of two explorative q-PoIs (q-PoI one and q-PoI worst ) on high-dimensional problems with difficult-to-approximate Pareto front boundaries.

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Section: Discussionmentioning

confidence: 99%

A Parallel Technique for Multi-objective Bayesian Global Optimization: Using a Batch Selection of Probability of Improvement

Yang¹,

Dong²,

Affenzeller³

2022

Preprint

Self Cite

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“…In some real-world optimisation scenarios, the objective function is very expensive to evaluate (such as in simulation-based optimisation) and surrogate models are used to approximate the fitness function. Werth et al [68] performed a preliminary investigation into landscape analysis on surrogate functions and found that the landscape features were more indicative of the surrogate model than the original landscape.…”

Section: Sampling and Robustness Of Measuresmentioning

confidence: 99%

“…Surrogate modelling has become an important technique for managing optimisation problems that have computationally expensive objective functions. Initial investigations into landscape analysis of surrogate functions [68] were not very successful and further work is needed to identify landscape analysis techniques that are suitable for characterising surrogate functions so that the analysis is indicative of the characteristics of the actual landscape.…”

Section: Opportunities For Further Researchmentioning

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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