Analysis of Noisy Evolutionary Optimization When Sampling Fails

In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{sample size}) independently, and its true fitness is then approximated by the average of these evaluations. Previous studies on sampling are mainly empirical. In this paper, we first investigate the effect of sample size from a theoretical perspective. By analyzing the (1+1)-EA on the noisy LeadingOnes problem, we show that as the sample size increases, the running time can reduce from exponential to polynomial, but then return to exponential. This suggests that a proper sample size is crucial in practice. Then, we investigate what strategies can work when sampling with any fixed sample size fails. By two illustrative examples, we prove that using parent or offspring populations can be better. Finally, we construct an artificial noisy example to show that when using neither sampling nor populations is effective, adaptive sampling (i.e., sampling with an adaptive sample size) can work. This, for the first time, provides a theoretical support for the use of adaptive sampling

Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint $k$. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter $d$ or budget $k$ allowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization.Comment: 17 pages, 1 tabl

Protective effect of quercetin on bupivacaine-induced neurotoxicity via T-type calcium channel inhibition

Purpose: To determine the effect of quercetin on bupivacaine-induced neurotoxicity and to investigate the mechanisms involved.Methods: Cultured SH-SY5Y cells were divided into five treatment groups: control group with no drug, bupivacaine treatment group, quercetin group, bupivacaine--quercetin combination treatment group, and bupivacaine-mibefradil combination treatment group. Cell morphology in each group was examined by microscopy while cell viability was assessed by 3-(4, 5-dimethyl-2-thiazolyl)-2, 5-diphenyl-2- tetrazolium bromide (MTT) assay after 24 h incubation. Cytosolic calcium ion concentration was determined by flow cytometry while Cav3.1 protein expression was evaluated by western blot.Results: Quercetin (50 μM) significantly (p < 0.05) protected SH-SYS5 cells from bupivacaine-induced cell apoptosis and also significantly reduced intracellular calcium ion concentration (p < 0.01) by approximately 40 %. Cav3.1 protein expression was normalized following quercetin treatment.Conclusion: These results show that quercetin reduces the neurotoxicity induced by bupivacaine, possibly through inhibition of T-type calcium channel. This finding implies a novel mechanism for neuroprotective effect of quercetin, and its potential for treating toxicity arising from the use of local anesthetic agents.Keywords: Quercetin, Bupivacaine, Local anaesthetic, Neuroprotection, Neurotoxicity, T-type calcium channe

Protein tyrosine phosphatase non-receptor type 2 as the therapeutic target of atherosclerotic diseases: past, present and future

Tyrosine-protein phosphatase non-receptor type 2(PTPN2), an important member of the protein tyrosine phosphatase family, can regulate various signaling pathways and biological processes by dephosphorylating receptor protein tyrosine kinases. Accumulating evidence has demonstrated that PTPN2 is involved in the occurrence and development of atherosclerotic cardiovascular disease. Recently, it has been reported that PTPN2 exerts an anti-atherosclerotic effect by regulating vascular endothelial injury, monocyte proliferation and migration, macrophage polarization, T cell polarization, autophagy, pyroptosis, and insulin resistance. In this review, we summarize the latest findings on the role of PTPN2 in the pathogenesis of atherosclerosis to provide a rationale for better future research and therapeutic interventions

Migrant Resettlement by Evolutionary Multi-objective Optimization

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly in recent years, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem. We implement MR-EMO using three MOEAs, the popular NSGA-II, MOEA/D as well as the theoretically grounded GSEMO. To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO-SR, using matrix-swap mutation and repair mechanism, which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO

Numerical simulation of the blasting vibration response of shallow buried tunnel in complex urban environment

Base on the phase I project of Nanjing metro line IV, the blasting vibration response of shallow buried tunnel in complex urban environment was studied with ANSYS/LS-DYNA, the real load change was simulated with the loading way of measured stress curve, and there was a consistent between numerical simulation results and the measured data. The numerical results indicated that the velocity distribution in different directions were close in the close area (0-2 m); the vertical seismic wave attenuated at the fastest speed in the transferring process, and the radial seismic wave attenuated the fastest in the excavation direction ; in the distance from 2 m to 5 m, the tangential and radial vibration of the initiating side were both obviously larger than the other side of the core tube, which was still more violent than the vertical vibration, and the difference decreases with the distance increases. In the surrounding rocks , the radial vibration velocity was the biggest and attenuated at the fastest speed, which was close to linear attenuation ;the tangential vibration velocity is the smallest with the a relatively gentler damping, the vertical vibration attenuated until 8 m and then increased gradually and the resultant velocity obeyed the exponential damping law