Information processing in the immune system
New ideas in optimization
Static scheduling algorithms for allocating directed task graphs to multiprocessors
ACM Computing Surveys (CSUR)
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
Sequential and Parallel Cellular Automata-Based Scheduling Algorithms
IEEE Transactions on Parallel and Distributed Systems
An Incremental Genetic Algorithm Approach to Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
Evolutionary Scheduling: A Review
Genetic Programming and Evolvable Machines
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
The immune and the chemical crossover
IEEE Transactions on Evolutionary Computation
Optimizing task schedules using an artificial immune system approach
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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We propose an efficient method of extracting knowledge when scheduling parallel programs onto processors using an artificial immune system (AIS). We consider programs defined by Directed Acyclic Graphs (DAGs). Our approach reorders the nodes of the program according to the optimal execution order on one processor. The system works in either learning or production mode. In the learning mode we use an immune system to optimize the allocation of the tasks to individual processors. Best allocations are stored in the knowledge base. In the production mode the optimization module is not invoked, only the stored allocations are used. This approach gives similar results to the optimization by a genetic algorithm (GA) but requires only a fraction of function evaluations.