Hybrid Evolutionary Strategy Optimization for Port Tugboat Operation Scheduling

2009 ◽  
Author(s):  
Zhixiong Liu
Keyword(s):  
Evolutionary Strategy ◽  
Operation Scheduling

Placement of Digital Microfluidic Biochips via a New Evolutionary Algorithm

2021 ◽  
Vol 26 (6) ◽  
pp. 1-22
Author(s):  
Chen Jiang ◽  
Bo Yuan ◽  
Tsung-Yi Ho ◽  
Xin Yao
Keyword(s):  
Evolutionary Algorithm ◽  
Resource Constraints ◽  
Scheduling Algorithm ◽  
Search Space ◽  
Placement Problem ◽  
Heuristic Scheduling ◽  
Microfluidic Biochips ◽  
Feasible Solutions ◽  
Digital Microfluidic ◽  
Operation Scheduling

Digital microfluidic biochips (DMFBs) have been a revolutionary platform for automating and miniaturizing laboratory procedures with the advantages of flexibility and reconfigurability. The placement problem is one of the most challenging issues in the design automation of DMFBs. It contains three interacting NP-hard sub-problems: resource binding, operation scheduling, and module placement. Besides, during the optimization of placement, complex constraints must be satisfied to guarantee feasible solutions, such as precedence constraints, storage constraints, and resource constraints. In this article, a new placement method for DMFB is proposed based on an evolutionary algorithm with novel heuristic-based decoding strategies for both operation scheduling and module placement. Specifically, instead of using the previous list scheduler and path scheduler for decoding operation scheduling chromosomes, we introduce a new heuristic scheduling algorithm (called order scheduler) with fewer limitations on the search space for operation scheduling solutions. Besides, a new 3D placer that combines both scheduling and placement is proposed where the usage of the microfluidic array over time in the chip is recorded flexibly, which is able to represent more feasible solutions for module placement. Compared with the state-of-the-art placement methods (T-tree and 3D-DDM), the experimental results demonstrate the superiority of the proposed method based on several real-world bioassay benchmarks. The proposed method can find the optimal results with the minimum assay completion time for all test cases.

scholarly journals Effect of An 84-bp Deletion of the Receptor-Binding Domain on the ACE2 Binding Affinity of the SARS-CoV-2 Spike Protein: An In Silico Analysis

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 194
Author(s):  
Gábor Kemenesi ◽  
Gábor Endre Tóth ◽  
Dávid Bajusz ◽  
György M. Keserű ◽  
Gabriella Terhes ◽  
...  
Keyword(s):  
Receptor Binding ◽  
Early Recognition ◽  
In Silico Analysis ◽  
Genetic Alterations ◽  
Binding Domain ◽  
Spike Protein ◽  
Evolutionary Strategy ◽  
Receptor Binding Domain ◽  
Base Pair Deletion ◽  
Therapeutic Research

SARS-CoV-2 is a recently emerged, novel human coronavirus responsible for the currently ongoing COVID-19 pandemic. Recombination is a well-known evolutionary strategy of coronaviruses, which may frequently result in significant genetic alterations, such as deletions throughout the genome. In this study we identified a co-infection with two genetically different SARS-CoV-2 viruses within a single patient sample via amplicon-based next generation sequencing in Hungary. The recessive strain contained an 84 base pair deletion in the receptor binding domain of the spike protein gene and was found to be gradually displaced by a dominant non-deleterious variant over-time. We have identified the region of the receptor-binding domain (RBD) that is affected by the mutation, created homology models of the RBDΔ84 mutant, and based on the available experimental data and calculations, we propose that the mutation has a deteriorating effect on the binding of RBD to the angiotensin-converting enzyme 2 (ACE2) receptor, which results in the negative selection of this variant. Extending the sequencing capacity toward the discovery of emerging recombinant or deleterious strains may facilitate the early recognition of novel strains with altered phenotypic attributes and understanding of key elements of spike protein evolution. Such studies may greatly contribute to future therapeutic research and general understanding of genomic processes of the virus.

scholarly journals Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2269-2282
Author(s):  
D Mester ◽  
Y Ronin ◽  
D Minkov ◽  
E Nevo ◽  
A Korol
Keyword(s):  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Simulated Data ◽  
Real Data ◽  
Genetic Maps ◽  
Chromosome 1 ◽  
Evolutionary Strategy ◽  
Group A ◽  
The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

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