2019 Evolutionary Algorithms Review

Sloss, Andrew N.; Gustafson, Steven

doi:10.1007/978-3-030-39958-0_16

Cited by 67 publications

(40 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These methods fall under the scope of reinforcement learning, adapting processes for optimal performance by reinforcing desired behavior (27). Of special interest are the genetic algorithms (GA), which model the mechanisms of darwinistic evolution in a computational algorithm, utilizing genetic elements such as recombination, cross-over, mutation, selection, and fitness (28). Since simulation of evolution is both time and labor intensive, it can quickly become intractable in a laboratory setting thereby limiting its application.…”

Section: Evolutionary Molecular Dynamics (Evo-md)mentioning

confidence: 99%

Physics-based inverse design of cholesterol attracting transmembrane helices reveals a paradoxical role of hydrophobic length

Methorst

2021

Preprint

View full text Add to dashboard Cite

The occurrence of linear cholesterol-recognition motifs in alpha-helical transmembrane domains has long been debated. Here, we demonstrate the ability of a genetic algorithm guided by coarse-grained molecular dynamics simulations---a method coined evolutionary molecular dynamics (evo-MD)---to directly resolve the sequence which maximally attracts/sorts cholesterol within a single-pass alpha-helical transmembrane domain (TMDs). We illustrate that the evolutionary landscape of cholesterol attraction in membrane proteins is characterized by a sharp, well-defined global optimum. Surprisingly, this optimal solution features an unusual short hydrophobic block, consisting of typically only eight short chain hydrophobic amino acids, surrounded by three successive lysines. Owing to the membrane thickening effect of cholesterol, cholesterol-enriched ordered phases favor TMDs characterized by a long rather than a short hydrophobic length. However, this short hydrophobic pattern evidently offers a pronounced net advantage for the binding of free cholesterol in both coarse-grained and atomistic simulations. Attraction is mediated by the unique ability of cholesterol to snorkel within the hydrophobic core of the membrane and thereby shield deeply located lysines from the unfavorable hydrophobic surrounding. Since this mechanism of attraction is of a thermodynamic nature and is not based on molecular shape specificity, a large diversity of sub-optimal cholesterol attracting sequences can exist. The puzzling sequence variability of proposed linear cholesterol-recognition motifs is thus consistent with sub-optimal, unspecific binding of cholesterol. Importantly, since evo-MD uniquely enables the targeted design of recognition motifs for distinct fluid lipid membranes, we foresee wide applications for evo-MD in the biological and biomedical fields.

show abstract

Section: Evolutionary Molecular Dynamics (Evo-md)mentioning

confidence: 99%

Physics-based inverse design of cholesterol attracting transmembrane helices reveals a paradoxical role of hydrophobic length

Methorst

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Namely, the empirical distribution was applied at the step of initializing the population in order to avoid falling into the local (yet not the global) optima. Indeed, such a situation is quite often a problem typical for genetic algorithms (Sloss and Gustafson 2019). In such cases, if the initial population were (randomly) grouped around one of the local (yet not the global) optima, the solutions generated by a genetic algorithm would start striving toward it and ignore the global optimum.…”

Section: Energy Commoditiesmentioning

confidence: 99%

“…Nevertheless, since the time of their publishing, the applications of the described methods in econometrics have developed significantly. A broad area of the subject is covered in the article by Sloss and Gustafson (2019), whereas Drake and Marks (2002) discussed some forecasting techniques in finance and macroeconomics employing genetic algorithms. They also presented a cursory review of research concerning certain commodity forecasts because their paper concerned multiple topics in economics and financial forecasting.…”

Section: Introductionmentioning

confidence: 99%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

View full text Add to dashboard Cite

This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

show abstract

“…It is impossible to obtain a strict solution to an optimization problem in the conditions of chaotic dynamics. Because of this, we implemented a numerical iterative optimization based on evolutionary modeling [23][24][25][26][27][28]. In essence, this method was a modification of random search inspired by Darwinian evolution [23].…”

Section: Evolutionary Adaptation Of Our Multiregressional Asset Management Strategy Algorithmmentioning

confidence: 99%

Analyzing, Modeling, and Utilizing Observation Series Correlation in Capital Markets

Мусаев

Grigoriev

2021

Computation

View full text Add to dashboard Cite

In this paper, we consider the task of the analysis, modeling, and application of dependencies between asset quotes at various capital markets. As an example, we study the dependency between financial instrument observation series in the currency and stock markets. Our work intends to give a theoretical basis to asset management strategies that estimate an asset’s price via regression, taking into account its correlated assets in various markets. Furthermore, we provide a way to increase the estimate quality using an evolutionary algorithm.

show abstract

2019 Evolutionary Algorithms Review

Cited by 67 publications

References 22 publications

Physics-based inverse design of cholesterol attracting transmembrane helices reveals a paradoxical role of hydrophobic length

Physics-based inverse design of cholesterol attracting transmembrane helices reveals a paradoxical role of hydrophobic length

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Analyzing, Modeling, and Utilizing Observation Series Correlation in Capital Markets

Contact Info

Product

Resources

About