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State-of-the-art distributed computational environments requires increasingly flexible and efficient workflow scheduling procedures in order to satisfy the increasing requirements of the scientific community. In this paper, we present a novel, nature-inspired scheduling approach based on the leveraging of inherited populations in order to increase the quality of generated planning solutions for the occurrence of system events such as a computational resources crash or a task delay with the rescheduling phase .The proposed approach is based on a hybrid algorithm which was described in our previous work and includes strong points of list-based heuristics and evolutionary meta-heuristics principles. In this paper we also experimentally show that the proposed extension of hybrid algorithms generates more effective solutions than the basic one in dynamically heterogeneous computational changing environments.

Evolutionary Inheritance in Workflow Scheduling Algorithms within Dynamically Changing Heterogeneous Environments