ERNEAD: Training of Artificial Neural Networks Based on a Genetic Algorithm and Finite Automata Theory
Abstract
This paper presents a variation in the algorithm EMODS (Evolutionary Metaheuristic of Deterministic Swapping), at the level of its mutation stage in order to train algorithms for
each problem. It should be noted that the EMODS metaheuristic is a novel framework that allows multi-objective optimization of combinatorial problems. The proposal for the
training of neural networks will be named ERNEAD (training of Evolutionary Neural Networks through Evolutionary Strategies and Finite Automata). The selection process consists of five phases: the initial population generation phase, the forward feeding phase of the network, the EMODS search phase, the crossing and evaluation phase, and finally the verification phase. The application of the process in the neural networks will generate sets of networks with optimal weights for a particular problem. ERNEAD algorithm was applied to two typical problems: breast cancer and flower classification, the solution of the problems were compared with solutions obtained by applying the classical Backpropagation, Conjugate Gradient and Levenberg-Marquardt algorithms. The analysis of the results indicated that ERNEAD produced more precise solutions than the ones thrown by the classic algorithms.
Keywords
Full Text:
PDFDisclaimer/Regarding indexing issue:
We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.