Genetic algorithms with exons and introns for the satisfiability problem

Popov, Vladimir

doi:10.12988/astp.2013.13034

Cited by 12 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, pretreatment was an essential step of computational modeling before variable selection from the exhaustive data set of descriptors. The classic method of selection of variables based on the theory of evolution of Darwin, wherein various smaller data sets of mutations and crossover studies, finally the offspring, are again correlated with parents, giving the relevant set of descriptors giving crucial information on drugs . The final prediction of phenomenon of particle size was dependent on PLS-DA modeling, wherein computation of the latent variables from the given X -variables and Y as response variable (particle size).…”

Section: Discussionmentioning

confidence: 99%

Investigating Key Molecular Descriptors Affecting Particle Size: A Predictive Exemplary Approach for Self-Emulsifying System

et al. 2023

View full text Add to dashboard Cite

The self-nano/microemulsifying drug delivery system is one of the well-established techniques for enhancing the solubility of poorly water-soluble drug molecules. The ratio of oil:surfactant:cosolvent plays a key role in globule size on dispersion into water, but there is very limited information on how a drug molecule affects the size. The rationale of this project was to illustrate the correlation between the particle size of nanoemulsion droplets and molecular descriptors of a drug. In the study, a self-nanoemulsifying preconcentrate containing drug with medium chain triglycerides (oil), dimethylacetamide (DMA, cosolvent), and Kolliphor EL (surfactant) was prepared for 40 drug molecules with diverse physicochemical properties. The selfnanoemulsifying preconcentrate was dispersed in water, and dynamic light scattering particle size was analyzed. A majority of drugs showed a significant increase in globule size compared to blank formulation, while few drugs showed a stark reduction in globule size. It is interesting to understand the attributes of molecules driving the self-emulsification and the diameter of nanoglobules. A systematic correlation of resultant particle size with 1D, 2D, and 3D molecular descriptors (overall more than 700 descriptors) was carried out for the data set using the PaDEL tool kit. The data compilation, curation, and analysis were performed using the SIMCA14 software. In the process of molecular descriptors screening, thereafter curation, 50 descriptors were selected using the genetic algorithm screening. The PLS-DA statistical method was employed for conversion of data into binomial systems. Final group of 5 descriptors: SpMiSpMin2_Bhe, RNCS, TDB9i, JG17, and ETA_Shape showed the correlation with particle size and classifying the drug molecules facilitating increase or decrease in particle size.

show abstract

Section: Discussionmentioning

confidence: 99%

Investigating Key Molecular Descriptors Affecting Particle Size: A Predictive Exemplary Approach for Self-Emulsifying System

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In particular, we have used algorithms A [1] (see [24]), A [2] (see [25]), A [3] (see [26]), A [4] (see [27]), A [5] (see [28]), A [6] (see [29]) for the satisfiability problem.…”

Section: Computational Experiments For Satisfiability Algorithmsmentioning

confidence: 99%

The discrete minimum constraint removal motion planning problem

Gorbenko

Popov

2015

AIP Conference Proceedings

Self Cite

View full text Add to dashboard Cite

A minimum time problem revisited Am.Abstract. Many planning problems for robots are of considerable interest. In this paper, we consider the discrete minimum constraint removal motion planning problem that can be used for a motion planning formulation with explanations for failure. We consider an efficient approach to solve the problem. In particular, we consider an explicit reduction from the decision version of the problem to the satisfiability problem. We present the results of computational experiments for different satisfiability algorithms.

show abstract

“…Also, we consider A1 GSAT with adaptive score function (see [5]); A2 genetic algorithm with exons and introns (see [6]);…”

Section: Introductionmentioning

confidence: 99%

On the valid deterministic localization plan problem

Sheka¹

2013

ams

View full text Add to dashboard Cite

Grid graphs are popular testbeds for planning with incomplete information. In particular, it is studied a fundamental planning problem, localization, to investigate whether gridworlds make good testbeds for planning with incomplete information. It is found empirically that greedy planning methods that interleave planning and plan execution can localize robots very quickly on random gridworlds or mazes. Thus, they may not provide adequately challenging testbeds. On the other hand, it is showed that finding localization plans that are within a log factor of optimal is NP-hard. Thus there are instances of gridworlds on which all greedy planning methods perform very poorly. These theoretical results help empirical researchers to select appropriate planning methods for planning with incomplete information as well as testbeds to demonstrate them. However, for practical application of difficult instances we need a method for their fast decision. In this paper, we consider an approach to solve localization problem. In particular, we consider an explicit polynomial reduction from the decision version of the valid deterministic localization plan problem to the 3-satisfiability problem. Mathematics Subject Classification: 68T40

show abstract

Genetic algorithms with exons and introns for the satisfiability problem

Abstract: In this paper we propose a new model of genetic algorithms. This model uses notions of exons and introns. We consider the satisfiability problem as a testbed for a genetic algorithm with exons and introns.

Cited by 12 publications

References 4 publications

Investigating Key Molecular Descriptors Affecting Particle Size: A Predictive Exemplary Approach for Self-Emulsifying System

Investigating Key Molecular Descriptors Affecting Particle Size: A Predictive Exemplary Approach for Self-Emulsifying System

The discrete minimum constraint removal motion planning problem

On the valid deterministic localization plan problem

Contact Info

Product

Resources

About