Assessment and Comparison of Evolutionary Algorithms for Tuning a Bio-Inspired Retinal Model

Crespo-Cano, Rubén; Martínez-Álvarez, Antonio; Cuenca-Asensi, Sergio; Fernández, Eduardo

doi:10.1007/978-3-319-59740-9_10

Cited by 1 publication

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“…All parts of the mathematical model have many parametrical candidates to be adjusted, most of which are real numbers with, in reality, an infinite range of possible values. Therefore, a straightforward understanding of the fine-tuning of those parameters represents a difficult problem that is addressed through an extension of our previous work [6,7], in which an automatic population-based multi-objective strategy was proposed to optimise model performance. We now propose a strategy that consists of applying metaheuristic optimisation algorithms to fit the model together with analytical tools for evaluating and comparing the results of each metaheuristic process.…”

Section: Introductionmentioning

confidence: 99%

Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model

Crespo-Cano

Cuenca-Asensi

Fernández

et al. 2019

Sensors

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A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics—genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)—are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.

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Section: Introductionmentioning

confidence: 99%