Active Processor Scheduling Using Evolution Algorithms

The allocation of processes to processors has long been of interest to engineers. The processor allocation problem considered here assigns multiple applications onto a computing system. With this algorithm researchers could more efficiently examine real-time sensor data like that used by United States Air Force digital signal processing efforts or real-time aerosol hazard detection as examined by the Department of Homeland Security. Different choices for the design of a load balancing algorithm are examined in both the problem and algorithm domains. Evolutionary algorithms are used to find near-optimal solutions. These algorithms incorporate multiobjective coevolutionary and parallel principles to create an effective and efficient algorithm for real-world allocation problems. Three evolutionary algorithms (EA) are developed. The primary algorithm generates a solution to the processor allocation problem. This allocation EA is capable of evaluating objectives in both an aggregate single objective and a Pareto multiobjective manner. The other two EAs are designed for fine turning returned allocation EA solutions. One coevolutionary algorithm is used to optimize the parameters of the allocation algorithm. This meta-EA is parallelized using a coarse-grain approach to improve performance. Experiments are conducted that validate the improved effectiveness of the parallelized algorithm. Pareto multiobjective approach is used to optimize both effectiveness and efficiency objectives. The other coevolutionary algorithm generates difficult allocation problems for testing the capabilities of the allocation EA. The effectiveness of both coevolutionary algorithms for optimizing the allocation EA is examined quantitatively using standard statistical methods. Also the allocation EAs objective tradeoffs are analyzed and compared

A General Model of Co-evolution for Genetic Algorithms

. Compared with natural systems, Genetic Algorithms have a limited adaptive capacity, i.e. they get quite frequently trapped at local optima and they are poor at tracking moving optima in dynamic environments. This paper describes a general, formal model of co-evolution, the Linear Model of Symbiosis, that allows for the concise, unified expression of all types of coevolutionary relations studied in ecology. Experiments on several difficult problems support our assumption that the addition of the Linear Model of Symbiosis to a canonical Genetic Algorithm can remedy the above shortcomings. 1 Introduction Although Genetic Algorithms (GAs) have demonstrated their robustness and efficiency as search and learning techniques in many application domains, they often suffer from the problem of premature convergence: as the individuals in an evolving population approach a local optimum, the resulting loss of genetic diversity may prevent the GA from finding the global optimum 1 . A related ..