PEGA:probabilistic environmental gradient-driven genetic algorithm considering epigenetic traits to balance global and local optimizations

Epigenetics’flexibility in terms of finer manipulation of genes renders unprecedented levels of refined and diverse evolutionary mechanisms possible.From the epigenetic perspective,the main limitations to improving the stability and accuracy of genetic algorithms are as follows:(1)the unchangeable nature of the external environment,which leads to excessive disorders in the changed phenotype after mutation and crossover;(2)the premature convergence due to the limited types of epigenetic operators.In this paper,a probabilistic environmental gradientdriven genetic algorithm(PEGA)considering epigenetic traits is proposed.To enhance the local convergence efficiency and acquire stable local search,a probabilistic environmental gradient(PEG)descent strategy together with a multi-dimensional heterogeneous exponential environmental vector tendentiously generates more offsprings along the gradient in the solution space.Moreover,to balance exploration and exploitation at different evolutionary stages,a variable nucleosome reorganization(VNR)operator is realized by dynamically adjusting the number of genes involved in mutation and crossover.Based on the above-mentioned operators,three epigenetic operators are further introduced to weaken the possible premature problem by enriching genetic diversity.The experimental results on the open Congress on Evolutionary Computation-2017(CEC’17)benchmark over 10-,30-,50-,and 100-dimensional tests indicate that the proposed method outperforms 10 state-of-the-art evolutionary and swarm algorithms in terms of accuracy and stability on comprehensive performance.The ablation analysis demonstrates that for accuracy and stability,the fusion strategy of PEG and VNR are effective on 96.55%of the test functions and can improve the indicators by up to four orders of magnitude.Furthermore,the performance of PEGA on the real-world spacecraft trajectory optimization problem is the best in terms of quality of the solution.

多中心联合病例汇报与讨论教学法在肾脏病科继续医学教育中的应用

肾脏病学是一门涉及多学科、较为复杂的学科,继续医学教育的难度较大。在肾脏病专业年轻医师临床实践能力和综合素质的培养中,现有的继续教育模式相对滞后。本文率先提出、实施一种新型多中心联合病例教学法:利用互联网视频会议方式与多家医院联合进行病例汇报、专家讨论与点评。与传统教学查房和单中心病例汇报相比,该新型教学模式能促使汇报人更加积极地参与到病史采集、初步诊断与鉴别诊断、治疗方案制定和最新研究文献检索等过程,并通过讨论和争鸣更牢固地掌握和拓展知识技能,从而提高临床诊治水平。

Adaptive feedback control for nonlinear triangular systems subject to uncertain asymmetric dead-zone input

In this paper,an adaptive control strategy is proposed to investigate the issue of uncertain dead-zone input for nonlinear triangular systems with unknown nonlinearities.The considered system has no precise priori knowledge about the dead-zone feature and growth rate of nonlinearity.Firstly,a dynamic gain is introduced to deal with the unknown growth rate,and the dead-zone characteristic is processed by the adaptive estimation approach without constructing the dead-zone inverse.Then,by virtue of hyperbolic functions and sign functions,a new adaptive state feedback controller is proposed to guarantee the global boundedness of all signals in the closed-loop system.Moreover,the uncertain dead-zone input problem for nonlinear upper-triangular systems is solved by the similar control strategy.Finally,two simulation examples are given to verify the effectiveness of the control scheme.

H_(∞)output feedback control for large-scale nonlinear systems with time delay in both state and input

The H_(∞)output feedback control problem for a class of large-scale nonlinear systems with time delay in both state and input is considered in this paper.It is assumed that the interconnected nonlinearities are limited by constant multiplied by unmeasured states,delayed states and external disturbances.Different from existing methods to study the H_(∞)control of large-scale nonlinear systems,the static gain control technique is utilized to obtain an observer-based output feedback control strategy,which makes the closed-loop system globally asymptotically stable and attenuates the effect of external disturbances.An example is finally carried out to show the feasibility of the proposed control strategy.