Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060516.
Ning Zhang, Yubao Yan
College of Computer and Artificial Intelligence, Changzhou University, Changzhou, Jiangsu, 213100, China
In order to solve the problem that the classification effect of approximate logic dendritic neuron model is largely limited by the training effect of learning algorithm. The differential evolution algorithm of the population evolution algorithm is selected as the training algorithm of the model. Differential evolution algorithm is a branch of population evolution algorithm, which has the advantages of good robustness and easy implementation. But it also has the disadvantage that it is easy to fall into the stagnation of evolution. In order to solve this key problem, several differential evolution algorithms are investigated. Finally, we noticed that a differential evolution algorithm improved by selection operator can better solve the problem of algorithm evolution stagnation. This paper studies the training of logic dendritic neuron model using the differential evolution algorithm improved by this selection operator. In order to evaluate the performance of the algorithm training model, four representative data sets were used for experiments. When comparing the classification effect, particle swarm optimization algorithm, traditional differential evolution algorithm and genetic algorithm are selected as the comparison experiment.
artificial neural network, approximate logic dendritic neuron model, population evolution algorithm, differential evolution algorithm, classification
Ning Zhang, Yubao Yan. Approximate Logical Dendritic Neuron Model Based on Selection Operator Improved Differential Evolution Algorithm. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 113-122. https://doi.org/10.25236/AJCIS.2023.060516.
 Yegnanarayana B. Artificial neural networks. PHI Learning Pvt. Ltd., 2009.
 McCulloch W S, Pitts W. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 1943, 5: 115-133.
 Rosenblatt F. The perceptron - a perceiving and recognizing automaton. 1957.
 London M, Häusser M. Dendritic computation. Annu. Rev. Neurosci., 2005, 28: 503-532.
 Niell C M, Meyer M P, Smith S J. In vivo imaging of synapse formation on a growing dendritic arbor. Nature neuroscience, 2004, 7(3): 254-260.
 Koch C, Poggio T, Torre V. Retinal ganglion cells: a functional interpretation of dendritic morphology. Philosophical Transactions of the Royal Society of London, 1982, 298(1090):227.
 Koch C, Poggio T, Torre V. Nonlinear interactions in a dendritic tree: localization, timing, and role in information processing. Proceedings of the National Academy of Sciences, 1983, 80(9):2799-2802.
 W.R. Taylor, S. He, W.R. Levick, D.I. Vaney, Dendritic computation of direction selectivity by retinal ganglion cells, Science (5488) (2000) 2347–2350.
 Segev I. Sound grounds for computing dendrites. Nature, 1998, 393(6682):207-8.
 H.C. Dringenberg, B. Hamze, A. Wilson, W. Speechley, M.-C. Kuo, Heterosynaptic facilitation of in vivo thalamocortical long-term potentiation in the adult rat visual cortex by acetylcholine, Cerebral Cortex17 (4) (2006) 839–848.
 A. Losonczy, J.K. Makara, J.C. Magee, Compartmentalized dendritic plasticity and input feature storage in neurons,.Nature 452 (7186) (2008) 436.
 Sjostrom P J, Rancz E A, Roth A, et al. Dendritic excitability and synaptic plasticity. Physiological Reviews, 2008, 88(2):769-840.
 Makara J K, Losonczy A, Wen Q, et al. Experience-dependent compartmentalized dendritic plasticity in rat hippocampal CA1 pyramidal neurons. Nature Neuroscience, 2009, 12(12):1485-7.
 Legenstein R, Maass W. Branch-specific plasticity enables self-organization of nonlinear computation in single neurons. Journal of Neuroscience, 2011, 31(30):10787-10802.
 Costa Rui. One Cell to Rule Them All, and in the Dendrites Bind Them. Frontiers in Synaptic Neuroscience, 2011, 3:5.
 Sha Z, Lin H U, Todo Y, et al. 14-A Breast Cancer Classifier Using a Neuron Model with Dendritic Nonlinearity. 2016. 1365–1376.
 Zhou T, Gao S, Wang J, et al. Financial time series prediction using a dendritic neuron model. Knowledge-Based Systems, 2016, 105(aug.):214-224.
 Todo Y, Tamura H, Yamashita K, et al. Unsupervised learnable neuron model with nonlinear interaction on dendrites. Neural Networks, 2014, 60:96-103.
 Ji J, Gao S, Cheng J, et al. An approximate logic neuron model with a dendritic structure. Neurocomputing, 2015, 173(P3):1775-1783.
 Storn R, Price K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997, 11(4): 341.
 Liu Q, Du S, Wyk B, et al. Double-layer-clustering differential evolution multimodal optimization by speciation and self-adaptive strategies. Information Sciences, 2021, 545(1):465-486.
 Hameed A, Aboobaider B, Mutar M, et al. A new hybrid approach based on discrete differential evolution algorithm to enhancement solutions of quadratic assignment problem. International Journal of Industrial Engineering Computations, 2020, 11(1): 51-72.
 Xu B, Zhang H, Zhang M, et al. Differential evolution using cooperative ranking-based mutation operators for constrained optimization. Swarm and Evolutionary Computation, 2019, 49: 206-219.
 Cheng J, Pan Z, Liang H, et al. Differential evolution algorithm with fitness and diversity ranking-based mutation operator. Swarm and Evolutionary Computation, 2021, 61: 100816.
 Zeng Z, Zhang M, Chen T, et al. A new selection operator for differential evolution algorithm. Knowledge-Based Systems, 2021, 226:107150.
 Gabbiani F, Krapp H G, Koch C, et al. Multiplicative computation in a visual neuron sensitive to looming. Nature. vol. 420, no. 6913, pp. 320–324, 2002.
 Bonyadi M R, Michalewicz Z. Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review. Evolutionary Computation, 2017, 25(1):1-54.
 Srinivas M, Patnaik L M. Adaptive probabilities of crossover and mutation in genetic algorithms. IEEE Transactions on Systems Man & Cybernetics, 2002, 24(4):656-667.
 Storn R, Price K. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces. Journal of Global Optimization, 1997, 11(4):341-359.