Meta-heuristically Seeded Genetic Algorithm for Independent Job Scheduling in Grid Computing Muhanad Tahrir Younis, Shengxiang Yang, and Benjamin Passow Centre for Computational Intelligence (CCI), School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester LE1 9BH, UK p14017957@my365.dmu.ac.uk, syang@dmu.ac.uk, benpassow@ieee.org Abstract. Grid computing is an infrastructure which connects geo- graphically distributed computers owned by various organizations allow- ing their resources, such as computational power and storage capabilities, to be shared, selected, and aggregated. Job scheduling problem is one of the most difficult tasks in grid computing systems. To solve this problem efficiently, new methods are required. In this paper, a seeded genetic al- gorithm is proposed which uses a meta-heuristic algorithm to generate its initial population. To evaluate the performance of the proposed method in terms of minimizing the makespan, the Expected Time to Compute (ETC) simulation model is used to carry out a number of experiments. The results show that the proposed algorithm performs better than other selected techniques. Keywords: meta-heuristic algorithms; seeded Genetic Algorithm; Ant Colony Optimization; job scheduling; grid computing; makespan 1 Introduction Grid Computing has been defined as a type of parallel and distributed infrastruc- ture which allows the geographically distributed autonomous and heterogeneous resources to be shared, selected and aggregated dynamically depending on their availability, capability, performance, cost, and users quality-of-service require- ments. This infrastructure offers to its users the same processing capabilities provided by supercomputers by creating a virtual supercomputer from connect- ing various networked and loosely coupled computers together allowing their resources to be shared among users. Computers, processing elements, software applications, printers, network interfaces, storage space and data are examples of resources. Middleware, computer software which provide basic services for re- source management, security, monitoring, and so forth, are used to connect all these resources to a network. Due to the fact that resources are owned by various administrative organizations, local policies are defined to specify what is shared, who is allowed to access what and when, and under what conditions. The Grid architecture is based on the creation of Virtual Organizations (VOs), a set of rules defined by individuals and institutions to control resource sharing [8]. By sharing some or all of its resources, a physical organization can be part of one or