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- 实验方案
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Understanding Measurement Artifacts Causing Inherent Cation Gradients in Depth Profiles of Perovskite Photovoltaics with TOF-SIMS
摘要： Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC’13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.
关键词： evolution path (EP),Cumulative learning,evolutionary computation,differential evolution (DE)
更新于2025-09-23 15:19:57
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Buried-ridge-waveguide Type GaInAsP/InP Membrane Distributed-Reflector Lasers for Reduction of Differential Resistance
摘要： Utilizing cumulative correlation information already existing in an evolutionary process, this paper proposes a predictive approach to the reproduction mechanism of new individuals for differential evolution (DE) algorithms. DE uses a distributed model (DM) to generate new individuals, which is relatively explorative, whilst evolution strategy (ES) uses a centralized model (CM) to generate offspring, which through adaptation retains a convergence momentum. This paper adopts a key feature in the CM of a covariance matrix adaptation ES, the cumulatively learned evolution path (EP), to formulate a new evolutionary algorithm (EA) framework, termed DEEP, standing for DE with an EP. Without mechanistically combining two CM and DM based algorithms together, the DEEP framework offers advantages of both a DM and a CM and hence substantially enhances performance. Under this architecture, a self-adaptation mechanism can be built inherently in a DEEP algorithm, easing the task of predetermining algorithm control parameters. Two DEEP variants are developed and illustrated in the paper. Experiments on the CEC’13 test suites and two practical problems demonstrate that the DEEP algorithms offer promising results, compared with the original DEs and other relevant state-of-the-art EAs.
关键词： evolutionary computation,differential evolution (DE),evolution path (EP),Cumulative learning
更新于2025-09-19 17:13:59
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[IEEE 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro) - Sao Paulo, Brazil (2019.8.26-2019.8.30)] 2019 34th Symposium on Microelectronics Technology and Devices (SBMicro) - Structural optimization of a superlattice infrared photodetector by evolutionary computation algorithms
摘要： We present a study of the use of evolutionary computation in the design of a new superlattice infrared photodetector (SLIP). Four optimization algorithms were used to find the parameters of the superlattice, specifically the thickness of the quantum wells and quantum barriers, which give the desired detection energy with the highest possible oscillator strength. The initial parameters for optimization are of a reference SLIP with detection energy and corresponding oscillator strength equal to 300 meV and 0.22, respectively. All optimization algorithms converged to a new superlattice with an oscillator strength around 0.35 for the same detection, a value 59% greater than the reference SLIP.
关键词： Photodetector,Optimization,Intersubband Transition,Evolutionary Computation,Quantum Well
更新于2025-09-16 10:30:52
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Genetic Algorithm–Genetic Programming Approach to Identify Hierarchical Models for Ultraviolet Disinfection Reactors
摘要： The performance of ultraviolet (UV) disinfection reactors using experimental data poses major challenges to the water treatment industry, and a regression model has been developed in the water treatment industry to predict UV reactor performance. Genetic programming (GP) can be applied using a process of symbolic regression to create empirical models of data describing a process or system. While classical regression analysis specifies the model structure a priori, GP automatically evolves both the structure and numeric coefficients of the model. GP-derived equations are often computationally complex, however, and do not generalize well for new data sets. This research develops a new model identification procedure that simultaneously identifies an equation to describe a system and hierarchical parameters that are fit for separate data sets. A coupled genetic algorithm (GA) and genetic programming approach (GA-GP) is developed to search for the best-fitting model structure and hierarchical parameter values. Modifications were made to the GA-GP approach to reduce model error while limiting the growth of complex tree structures. The GA-GP method is applied here to identify models for multiple UV reactors by training a model for three data sets. The GA-GP method identified a model with lower error across multiple data sets compared to GP alone, linear regression, and the industry regression model. Including hierarchical terms allowed the search to identify a model that generalizes across multiple data sets.
关键词： System identification,Drinking water treatment,Evolutionary computation,Bloat,Symbolic regression
更新于2025-09-04 15:30:14