Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Who dictates corporate governance practices in Turkey? The role of ownership structure for XKURY companies

Abstract: This paper investigates the influence of ownership structure on corporate governance practices of Turkish companies listed in Borsa Istanbul Corporate Governance Index (XKURY). It proposes a contex...

Détournement in social media visuals for a shared activist identity and imagery

Abstract: This article aims to analyse some of the popular activist posters and images related to the Gezi Park resistance that took place in Turkey in 2013. The author argues that the theoretical approach o...

Competition and Product Innovation by Turkish Firms

Abstract: This chapter examines empirically the determinants of research and development (R&D) activities by Turkish firms. It focuses on the question of how competition affects product innovation, and not process innovation, in Turkey. In particular, we test if there is a non-linear relationship between R&D activities of a firm and the degree of competition in that industry. We use Turkish firm-level data from the Business Environment and Enterprise Performance Survey (BEEPS) and find strong support for an inverted-U relationship between the two variables.

An ensemble of differential evolution algorithms with variable neighborhood search for constrained function optimization

Abstract: In this paper, an ensemble of differential evolution algorithms based on a variable neighborhood search algorithm (EDE-VNS) is proposed so as to solve the constrained real parameter-optimization problems. The performance of DE algorithms heavily depends on the mutation strategies, crossover operators and control parameters employed. The proposed EDE-VNS algorithm employs multiple mutation operators and control parameters in its VNS loops to enhance the solution quality. In addition, we utilize opposition-based learning (OBL) to take advantages of opposite solutions to find a candidate solution which might be close to the global optimum. In addition, we also present an idea of injecting some good dimensional values from promising areas in the population to the trial individual through the injection procedure. The computational results show that the EDE-VNS algorithm is very competitive to some of the best performing algorithms from the literature.