Optimized Job Scheduling approach based on Genetic algorithms in smart Grid environment

The advances in communications and information technologies have been playing a major role in all aspects of our lives. One of those majors aspects that affect our daily lives is the power grids which lead to what we call Smart Grids. One of the major challenges in these grids is to optimize the consumption and resources. This paper presents an optimized job scheduling approach using genetic algorithm which provides a minimum cost for completing different tasks in a grid environment.  In grid environment different independent appliances are sharing the same resources depending on the availability of resources and the need of these appliances to run. There are different job scheduling approached starting from typical strategies, Ant Colony (AC) and Genetic Algorithm (GA). In this paper we present a cost optimized Genetic Algorithm approach for appliances job scheduling by considering different parameters like job duration time, the resources availability and the job priority to start. The proposed approach is tested using a simulator written in c++ programming language. The results show that the total saving in cost is better than the previous approaches

Decoding of Block Codes by using Genetic Algorithms and Permutations Set

Recently Genetic algorithms are successfully used for decoding some classes of error correcting codes. For decoding a linear block code C, these genetic algorithms computes a permutation p of the code generator matrix depending of the received word. Our main contribution in this paper is to choose the permutation p from the automorphism group of C. This choice allows reducing the complexity of re-encoding in the decoding steps when C is cyclic and also to generalize the proposed genetic decoding algorithm for binary nonlinear block codes like the Kerdock codes. In this paper, an efficient stop criterion is proposed and it reduces considerably the decoding complexity of our algorithm. The simulation results of the proposed decoder, over the AWGN channel, show that it reaches the error correcting performances of its competitors. The study of the complexity shows that the proposed decoder is less complex than its competitors that are based also on genetic algorithms