An-Najah Blogs ::
http://blogs.najah.edu/author/emp_2262
An-Najah Blogs :: en-usTue, 12 Nov 2019 02:11:40 ISTTue, 12 Nov 2019 02:11:40 ISTwebmaster@najah.eduwebmaster@najah.eduOptimization of Production Systems Using Genetic Algorithmshttp://blogs.najah.edu/staff/emp_2262/article/Optimization-of-Production-Systems-Using-Genetic-AlgorithmsPublished ArticlesThis paper presents a Genetic Algorithm for Production Systems Optimization GAPSO The GAPSO finds an ordering of Condition Elements CEs in the rules of a Production System PS that results in a near optimal PS with respect to execution time Finding such an ordering can be difficult since there is often a large number of ways to order CEs in the rules of a PS Additionally existing heuristics to order CEs in many cases conflict with each other The GAPSO is applicable to PSs in general and no assumptions are made about the matching algorithm or the interpreter that executes the PS The results of applying the GAPSO to some example PSs are presented In all examples the GAPSO found an optimal ordering of CEs in a small number of iterations
http:dxdoiorg101142S1469026803000987A Genetic Algorithm to Solve the Maximum Partition Problemhttp://blogs.najah.edu/staff/emp_2262/article/A-Genetic-Algorithm-to-Solve-the-Maximum-Partition-ProblemPublished ArticlesA maximum partition of a directed weighted graph is partitioning the nodes into two sets such that it maximizes the total weights of edges between the two sets In this study a genetic algorithm is proposed to solve the maximum partition problem Experiments performed on randomly generated graphs of different sizes show that the proposed algorithm converges to an optimal solution faster than the existing heuristic algorithmRun-Time Elimination of Dead-Rules in Forward-Chaining Rule-Based Programshttp://blogs.najah.edu/staff/emp_2262/article/Run-Time-Elimination-of-Dead-Rules-in-Forward-Chaining-Rule-Based-ProgramsPublished ArticlesThis paper presents an optimization method to improve execution time of forward-chaining rule based programs The improvement is achieved by deleting rules that finish firing during run-time The conditions of the deleted rules are not matched against working memory in later execution cycles and hence the execution time is reduced Information obtained from control and data-flow analyses is utilized to determine when rules finish firing during nm-time Since rules are deleted during run-time only after they finish firing the optimization does not change the semantics of the source program The optimization method can be n final step to other optimization methods The results of applying the optimization to three CLIPS rule-based programs are presented These results show significant improvement when the source program contains rules that require significant matching time and finish execution early during run-time