Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction

Gao, Shangce; Song, Shuangbao; Cheng, Jiujun; Todo, Yuki; Zhou, MengChu

doi:10.1109/tcbb.2017.2705094

Cited by 72 publications

(25 citation statements)

References 89 publications

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“…Hence determining the most efficient algorithm to find an optimal aggregation solution is being an area of research. Gao et al [Gao, Song, Cheng, et al (2018)] propose a multi objective evolutionary algorithm, to predict the three-dimensional structure of protein from its one-dimensional sequence. It operates upon an innovative solvent accessible surface area as third objective.…”

Section: Figure 1: Architecture Of a Wireless Multimedia Sensor Networkmentioning

confidence: 99%

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Janarthanan¹,

Kumar²

2019

Computers, Materials &Amp; Continua

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Efficient aggregation in wireless sensor nodes helps reduce network traffic and reduce energy consumption. The objective of this work Localization Based Evolutionary Routing (LOBER) is to achieve global optimization for aggregation and WMSN lifetime. Improved localization is achieved by a novel Centroid Based Octant Localization (CBOL) technique considering an arbitrary hexagonal region. Geometric principles of hexagon are used to locate the unknown nodes in the centroid positions of partitioned regions. Flower pollination algorithm, a meta heuristic evolutionary algorithm that is extensively applied in solving real life, complex and nonlinear optimization problems in engineering and industry is modified as Enhanced Flower Pollination Algorithm (EFPA) to fit into WMSN and enhance routing mechanism and ensure efficiency in data aggregation. The system is simulated using MATLAB and found to have a considerable improvement in the optimization process.

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Section: Figure 1: Architecture Of a Wireless Multimedia Sensor Networkmentioning

confidence: 99%

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Janarthanan¹,

Kumar²

2019

Computers, Materials &Amp; Continua

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“…This is mainly due to two reasons. First, evolutionary algorithms (EAs) are efficient tools for tackling optimization problems with different properties and challenges, such as large scale [5]- [7], dynamic [8], multimodal [9]- [11], multiobjective [12]- [14], and many objective [15]. Second, there is an increasing number of real-world optimizations requiring distributed approaches [16], [17] and data-driven approaches [18], because their objective functions (and/or constraints functions) are always expensive, computationally intensive, or time consuming to perform.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

Zhan

Wang

et al. 2021

IEEE Trans. Cybern.

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Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the performance of DDEAs relies on their surrogate quality and often deteriorates if the amount of available data decreases. To solve these problems, this article proposes a new DDEA framework with perturbation-based ensemble surrogates (DDEA-PES), which contain two efficient mechanisms. The first is a diverse surrogate generation method that can generate diverse surrogates through performing data perturbations on the available data. The second is a selective ensemble method that selects some of the prebuilt surrogates to form a final ensemble surrogate model. By combining these two mechanisms, the proposed DDEA-PES framework has three advantages, including larger data quantity, better data utilization, and higher surrogate accuracy. To validate the effectiveness of the proposed framework, this article provides both theoretical and experimental analyses. For the experimental comparisons, a specific DDEA-PES algorithm is developed as an instance by adopting a genetic algorithm as the optimizer and radial basis function neural networks as the base models. The experimental results on widely used benchmarks and an aerodynamic airfoil design real-world optimization problem show that the proposed DDEA-PES algorithm outperforms some state-of-the-art DDEAs. Moreover, when compared with traditional nondata-driven methods, the proposed DDEA-PES algorithm only requires about 2% computational budgets to produce competitive results.

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“…It is a biological-inspired, populationbased global optimization algorithm. Due to its simple concept, easy implementation, fast convergence, and excellent robustness, it has been more extensively utilized than other mainstream evolutionary algorithms, such as the genetic algorithm (GA) [28,29], the evolutionary strategy (ES) [30,31], and particle swarm optimization (PSO) [32] in recent years. DE is similar to the GA and ES but differs from them because a unique differential evolution operator is referenced in DE.…”

Section: Introductionmentioning

confidence: 99%

A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm

Wang

Gao

Wang

et al. 2020

Computational Intelligence and Neuroscience

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A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution (DE) algorithm training is proposed. According to the signal transmission order, a DNM can be divided into four parts: the synaptic layer, dendritic layer, membrane layer, and somatic cell layer. It can be converted to a logic circuit that is easily implemented on hardware by removing useless synapses and dendrites after training. This logic circuit can be designed to solve complex nonlinear problems using only four basic logical devices: comparators, AND (conjunction), OR (disjunction), and NOT (negation). To obtain a faster and better solution, we adopt the most popular DE for DMAS training. We have chosen five classification datasets from the UCI Machine Learning Repository for an experiment. We analyze and discuss the experimental results in terms of the correct rate, convergence rate, ROC curve, and the cross-validation and then compare the results with a dendritic neuron model trained by the backpropagation algorithm (BP-DNM) and a neural network trained by the backpropagation algorithm (BPNN). The analysis results show that the DE-DMAS shows better performance in all aspects.

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Incorporation of Solvent Effect into Multi-Objective Evolutionary Algorithm for Improved Protein Structure Prediction

Cited by 72 publications

References 89 publications

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Localization Based Evolutionary Routing (LOBER) for Efficient Aggregation in Wireless Multimedia Sensor Networks

Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates

A Dendritic Neuron Model with Adaptive Synapses Trained by Differential Evolution Algorithm

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