@inbook {Simula.simula.2780,
	title = {An Evolutionary and Automated Virtual Team Making Approach for Crowdsourcing Platforms},
	booktitle = {Cloud-based Software Crowdsourcing"},
	year = {2014},
	pages = {1-17},
	publisher = {Springer},
	organization = {Springer},
	chapter = {2},
	address = {.},
	abstract = {Crowdsourcing has demonstrated its capability of supporting various software development activities including development and testing as it can be seen by several successful crowdsourcing platforms such as TopCoder and uTest. However, to crowd source large-scale and complex software development and testing tasks, there are several optimization challenges to be addressed such as division of tasks, searching a set of registrants, and assignment of tasks to registrants. Since in crowdsourcing a task can be assigned to registrants geo-graphically distributed with various background, the quality of final task deliverables is a key issue. As the first step to improve the quality, we propose a systematic and automated approach to optimize the assignment of registrants in a crowdsourcing platform to a crowdsourcing task. The objective is to find the best fit of a group of registrants to the defined task. A few examples of factors forming the optimization problem include budget defined by the task submitter and pay expectation from a registrant, skills required by a task, skills of a registrant, task delivering deadline, and availability of a registrant. We first collected a set of commonly seen factors that have impact on the {\textquotedblleft}perfect matching{\textquotedblright} between tasks submitted and a virtual team that consists of a selected set of registrants. We then formulated the optimization objective as a fitness function-the heuristics used by search algorithms (e.g., Genetic Algorithms) to find an optimal solution. We empirically evaluated a set of well-known search algorithms in software engineering, along with the proposed fit-ness function, to identify the best solution for our optimization problem. Results of our experiments are very positive in terms of solving optimization problems in a crowdsourcing context.},
	author = {Yue, Tao and Ali, Shaukat and Wang, Shuai},
	editor = {Michael N. {Huhns} and Li, Wei and Tsai, Wei-Tek}
}
