Evolving FPGA-based robot controllers using an evolutionary

In this paper, a novel evolutionary algorithm for intrinsic hardware evolution of Field Programmable Gate Array (FPGA) controllers is presented. The main feature of the evolutionary algorithm consists of a mutation operator, in which the mutation rate is defined according to the fitness. Experimental results on a Kephera robot show that the algorithm proposed can successfully navigate the robot to avoid collision in an unknown/changing environment

A co-evolutionary differential evolution algorithm for solving min-max optimization problems implemented on GPU using C-CUDA

Several areas of knowledge are being benefited with the reduction of the computing time by using the technology of graphics processing units (GPU) and the compute unified device architecture (CUDA) platform. In case of evolutionary algorithms, which are inherently parallel, this technology may be advantageous for running experiments demanding high computing time. In this paper, we provide an implementation of a co-evolutionary differential evolution (DE) algorithm in C-CUDA for solving min–max problems. The algorithm was tested on a suite of well-known benchmark optimization problems and the computing time has been compared with the same algorithm implemented in C. Results demonstrate that the computing time can significantly be reduced and scalability is improved using C-CUDA. As far as we know, this is the first implementation of a co-evolutionary DE algorithm in C-CUDA

Few-shot learning for biotic stress classification of coffee leaves

In the last few years, deep neural networks have achieved promising results in several fields. However, one of the main limitations of these methods is the need for large-scale datasets to properly generalize. Few-shot learning methods emerged as an attempt to solve this shortcoming. Among the few-shot learning methods, there is a class of methods known as embedding learning or metric learning. These methods tackle the classification problem by learning to compare, needing fewer training data. One of the main problems in plant diseases and pests recognition is the lack of large public datasets available. Due to this difficulty, the field emerges as an intriguing application to evaluate the few-shot learning methods. The field is also relevant due to the social and economic importance of agriculture in several countries. In this work, datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation tasks. We achieved competitive results compared with the ones reported in the literature in the classification task, with accuracy values close to 96%. Furthermore, we achieved superior results in the severity estimation task, obtaining 6.74% greater accuracy than the baseline

Fuzzy TOPSIS for group decision making: A case study for accidents with oil spill in the sea

The selection of the best combat responses to oil spill in the sea when several alternatives have to be evaluated with different weights for each criterion consist of a multicriteria decision making (MCDM) problem. In this work, firstly the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is described. Secondly, its expansion known as fuzzy TOPSIS to handle uncertain data is presented. Next, based on fuzzy TOPSIS we propose a fuzzy TOPSIS for group decision making, which is applied to evaluate the ratings of response alternatives to a simulated oil spill. The case study was carried out for one of the largest Brazilian oil reservoirs. The results show the feasibility of the fuzzy TOPSIS framework to find out the best combat responses in case of accidents with oil spill in the sea