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In the paper, an evolutionary approach to test generation for functional BIST is considered. The aim of the proposed scheme is to minimize the test data volume by allowing the devices microprogram to test its logic, providing an observation structure to the system, and generating appropriate test data for the given architecture. Two methods of deriving a deterministic test set at functional level are suggested. The first method is based on the classical genetic algorithm with binary and arithmetic crossover and mutation operators. The second one uses genetic programming, where test is represented as a sequence of microoperations. In the latter case, we apply two-point crossover based on exchanging test subsequences and mutation implemented as random replacement of microoperations or operands. Experimental data of the program realization showing the efficiency of the proposed methods are presented.
This study suggests a new approach to EEG data classification by exploring the idea of using evolutionary computation to both select useful discriminative EEG features and optimise the topology of Artificial Neural Networks. An evolutionary algorithm
Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including nois
The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group.
Analogy plays an important role in creativity, and is extensively used in science as well as art. In this paper we introduce a technique for the automated generation of cross-domain analogies based on a novel evolutionary algorithm (EA). Unlike exist
Within this paper, the exploration of an evolutionary approach to an alternative CellLineNet: a convolutional neural network adept at the classification of epithelial breast cancer cell lines, is presented. This evolutionary algorithm introduces cont