Rustem Popa

"Dunarea de Jos" University of Galati Romania

Rustem Popa was born in Galati, Romania, in 1960. He received his masters degree from the Politehnica University, Bucharest, Romania, in 1984. In 1999, he obtained his PhD degree from the Dunarea de Jos University, Galati, Romania. He worked as a design engineer and then as a scientific researcher at the Research and Design Institute for Shipbuilding in Galati. Since 1990 he has been with the Dunarea de Jos University in Galaţi and, since 2002, he has been an associate professor at the Department of Electronics and Telecommunications. Dr Popa has more than 20 years’ teaching experience in the area of digital electronics, medical electronics and soft computing. He is the author and co-author of four books and over 50 journal and conference papers. His research interests include computational intelligence, evolvable hardware, digital signal processing and medical electronics. He was editor-in-chief of the Annals of Dunarea de Jos University of Galati, fascicle III – ISSN 1221-454X in 2006 and 2007. He was also an IPC member and reviewer for many international conferences.

Rustem Popa

1books edited

1chapters authored

Latest work with IntechOpen by Rustem Popa

Genetic Algorithms (GAs) are one of several techniques in the family of Evolutionary Algorithms - algorithms that search for solutions to optimization problems by "evolving" better and better solutions. Genetic Algorithms have been applied in science, engineering, business and social sciences. This book consists of 16 chapters organized into five sections. The first section deals with some applications in automatic control, the second section contains several applications in scheduling of resources, and the third section introduces some applications in electrical and electronics engineering. The next section illustrates some examples of character recognition and multi-criteria classification, and the last one deals with trading systems. These evolutionary techniques may be useful to engineers and scientists in various fields of specialization, who need some optimization techniques in their work and who may be using Genetic Algorithms in their applications for the first time. These applications may be useful to many other people who are getting familiar with the subject of Genetic Algorithms.

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