Fourier Representations for Black-Box Optimization over Categorical Variables

Dadkhahi, Hamid; Rios, Jesus; Shanmugam, Karthikeyan; Das, Payel

doi:10.1609/aaai.v36i9.21255

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…This approach, initially developed for pseudo-Boolean functions, has been extended to include categorical variables. [11].…”

Section: Surrogate Model For Combinatorial Optimizationmentioning

confidence: 99%

Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem

Vendi,

Verel,

Fonlupt

2024

Lecture Notes in Computer Science

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Combinatorial optimization problems can involve computationaly expensive fitness function, making their resolution challenging. Surrogate models are one of the effective techniques used to solve such black-box problems by guiding the search towards potentially good solutions. In this paper, we focus on the use of surrogate based on multinomial approaches, particularly based on Walsh functions, to tackle pseudo-Boolean problems. Although this approach can be effective, a potential drawback is the growth of the polynomial expansion with problem dimension. We introduce a method for analyzing real-world combinatorial black-box problems defined through numerical simulation. This method combines Walsh spectral analysis and polynomial regression. Consequently, we propose a sparse surrogate model that incorporates selected, relevant terms and is simpler to optimize. To demonstrate our approach, we apply it to the bus stop spacing problem, an exemplary combinatorial pseudo-Boolean challenge.

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“…This approach, initially developed for pseudo-Boolean functions, has been extended to include categorical variables. [11].…”

Section: Surrogate Model For Combinatorial Optimizationmentioning

confidence: 99%

Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem

Vendi,

Verel,

Fonlupt

2024

Lecture Notes in Computer Science

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show abstract

“…It seems natural that we want to select designs that minimize the free energy of our target structure. This fitness function is often used in conjunction with other metrics ( 29 , 30 , 39 ), but it has been used independently too ( 50 , 54 ).…”

Section: Introductionmentioning

confidence: 99%

Fitness functions for RNA structure design

Ward

Courtney

Rivas

2023

Nucleic Acids Research

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An RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20 years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. We find that maximizing probability leads to better results on synthetic RNA design puzzles and agrees more often than other fitness functions with natural sequences and structures, which were designed by evolution. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.

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“…It seems natural that we want to select designs that minimize the free energy of our target structure. This fitness function is often used in conjunction with other metrics [30, 29, 38], but it has been used independently too [47, 49].…”

Section: Introductionmentioning

confidence: 99%

Fitness Functions for RNA Structure Design

Ward

Courtney

Rivas

2022

Preprint

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An RNA design algorithm takes a target RNA structure and finds a sequence that folds into that structure. This is fundamentally important for engineering therapeutics using RNA. Computational RNA design algorithms are guided by fitness functions, but not much research has been done on the merits of these functions. We survey current RNA design approaches with a particular focus on the fitness functions used. We experimentally compare the most widely used fitness functions in RNA design algorithms on both synthetic and natural sequences. It has been almost 20 years since the last comparison was published, and we find similar results with a major new result: maximizing probability outperforms minimizing ensemble defect. The probability is the likelihood of a structure at equilibrium and the ensemble defect is the weighted average number of incorrect positions in the ensemble. Also, we observe that many recently published approaches minimize structure distance to the minimum free energy prediction, which we find to be a poor fitness function.

show abstract

Fourier Representations for Black-Box Optimization over Categorical Variables

Cited by 4 publications

References 39 publications

Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem

Sparse Surrogate Model for Optimization: Example of the Bus Stops Spacing Problem

Fitness functions for RNA structure design

Fitness Functions for RNA Structure Design

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