An-Najah National University

An-Najah Blogs


  • Monday, September 1, 2003
  • Optimization of Production Systems Using Genetic Algorithms
  • Published at:International Journal of Computational Intelligence and Applications (IJCIA), Year: 2003 Vol: 3 Issue: 3 (September 2003) Page: 233 - 248
  • This 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.

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  • Tuesday, January 1, 2002
  • A Genetic Algorithm to Solve the Maximum Partition Problem
  • Published at:Pakistan Journal of Applied Sciences 2(1): 71-73, 2002
  • A 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 algorithm.
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  • Friday, January 1, 1999
  • Run-Time Elimination of Dead-Rules in Forward-Chaining Rule-Based Programs
  • Published at:An-Najah Univ. J. Res., Vol. 13 (1999) 37-56
  • This 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.
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Wael Abdalleh Mustafa
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