A regeneratable dynamic differential evolution algorithm for neural networks with integer weights

Bao, Jiguang; Chen, Yu; Yu, Junzhi

doi:10.1631/jzus.c1000137

Cited by 6 publications

(4 citation statements)

References 27 publications

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“…This step was done to find out the best individual xbest, G of current generation, in which G is the index of generation. Bao et al (2010) stated that there are three main steps of DE, which are mutation, crossover, and selection [5]. These steps were performed sequentially and were repeated during the optimization cycle.…”

Section: Differential Evolutionmentioning

confidence: 99%

Producing Succinic Acid in Yeast using A Hybrid of Differential Evolution and Flux Balance Analysis

Rashid¹,

Choon²,

Mohamad³

et al. 2013

IJBSBT

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In the field of metabolic engineering, one of the primary goals is to maximize the production of a desired substance. However, to identify the set of gene deletions that will result to the desired production goals is difficult. This is due to the complexity of the regulatory cellular and metabolic network and also lack of good modeling and optimization tools. In this study, the optimization algorithm from previous works was implemented to identify the gene knockout on the result. The previous works faced the problem of inability to provide single run with two goals to maximize the biomass and the desired products. Besides that, the previous work also showed long computational time. In this study, a hybrid of Differential Evolution and Flux Balance Analysis (DEFBA) is proposed to solve the long computational time problem and provide an optimal set of gene knockout with high yield of the desired product. The case study in this research involved the production of succinic acid (also called as succinate) in yeast Saccharomyces cerevisiae. The results from this experiment included the list of knockout genes and the growth rate after the deletion. DEFBA had shown better results compared to the other methods. The identified list suggested gene modifications over several pathways which may be useful in solving challenging genetic engineering problems.

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Section: Differential Evolutionmentioning

confidence: 99%

Producing Succinic Acid in Yeast using A Hybrid of Differential Evolution and Flux Balance Analysis

Rashid¹,

Choon²,

Mohamad³

et al. 2013

IJBSBT

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“…DE's unique population memory capability gives the algorithm a strong global search capability and robust performance. As a highly efficient parallel search algorithm with few controlled parameters, the DE algorithm has been widely used in neuron networks [3,4], power systems [5,6], vehicle routing problems [7][8][9][10][11] and many other fields [12][13][14][15][16][17][18][19][20]. Like other intelligent algorithms, however, the DE algorithm also has disadvantages such as precocity, strong parameter dependence and difficulty in obtaining global optimum values for high-dimensional complex objective functions [21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm

Wang

et al. 2020

Applied Sciences

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To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinemutation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science.

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“…Moreover, they are usually larger than 1000 dimensions and solutions. Differential evolution (DE) which is considered as an important branch of the evolutionary algorithms is widely used to solve optimization problems in many fields [1][2][3]. Therefore, we are inspired to apply the differential algorithm to solve the multistage goal programming model of the DPPO problem.…”

Section: Introductionmentioning

confidence: 99%

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

Shi²,

et al. 2014

AMR

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The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

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A regeneratable dynamic differential evolution algorithm for neural networks with integer weights

Cited by 6 publications

References 27 publications

Producing Succinic Acid in Yeast using A Hybrid of Differential Evolution and Flux Balance Analysis

Producing Succinic Acid in Yeast using A Hybrid of Differential Evolution and Flux Balance Analysis

Adaptive Dynamic Disturbance Strategy for Differential Evolution Algorithm

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

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