Julia Naomi Rosenfield Boeira

Work place: IBM Brasil, Av. Dolores Alcaraz Caldas, 90, Praia de Belas, Porto Alegre, RS, zip-code: 90110-180, Brazil And PUCRS, Av Ipiranga 6681, Partenon, Porto Alegre, RS, zip-code: 90619-900, Brazil

E-mail: jnboeira@outlook.com


Research Interests: Computational Physics, Computer systems and computational processes, Artificial Intelligence, Physics & Mathematics


Julia Naomi R. Boeira was born in Porto Alegre, Brazil. Boeira graduated in materials engineering, 2013, with masters to be concluded by the end of 2016 on artificial neural networks applied to ceramic materials, at Federal University of Rio Grande do Sul, Porto Alegre, Brazil. Boeira is specialized in artificial intelligence and physics for game developing.

She has published a paper on neural networks applied to ballistic ceramics, highly applicable to game physics. She has worked as JAVA and C# SOFTWARE DEVELOPER for IBM Brazil, Porto Alegre, and is currently focused in finishing her masters degree.

Author Articles
The Effects of “Preferentialism” on a Genetic Algorithm Population over Elitism and Regular Development in a Binary F6 Fitness Function

By Julia Naomi Rosenfield Boeira

DOI: https://doi.org/10.5815/ijisa.2016.09.05, Pub. Date: 8 Sep. 2016

Mating preferentialism among animals is the natural form of elitism that has a higher genetic variance and a shorter number of interactions. This concept refers to fact that most animals cannot breed indefinitely – this is the case of elitism - and suffer DNA degradation. In this paper, two types of preferentialism were analyzed (mutation and second best); in both cases we found evidence of improvements over no-preferentialism or elitism. The best number of generations for preferentialism was determined to be 5, from a group of 3 to 20, with the smallest average of iterations and the most consistent average fitness. A sequencing of 0 to 7 was selected and used in association with mutation preferentialism in order to determine the best number of generations. In the case of BinaryF6, mutation preferentialism has a higher average best fitness (ABF) (0.9986) and a lower number of interactions (2259). Second best preferentialism has a better average last fitness (ALF) (0.6070) and a little higher number of interactions (3956). These results reveal that the two suggested form of preferentialism exhibit significant improvements in terms of time and result quality when they are compared with elitism (ABF of 0.9981, ALF of 0.6005 and an average number of interactions of 18197) or with no-preferentialism (ABF of 0.9982, ALF of 0.5177 and average number of interactions of 181088.

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