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Efficient rule based genetic algorithm

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  1. Abstract
  2. Introduction
  3. Expert system
  4. Fuzzy expert system
  5. Rule based system
  6. Efficient rule base
  7. Related work
  8. Future work
  9. Conclusion
  10. References

Genetic algorithm (GA) is an optimization technique which is applicable to all the functions that can be evaluated by using fuzzy rule based system. The problems can also be optimized by using mathematical functions such as calculus (derivative, integration etc.), and other nonlinear modeling tools such as neural networks. But the main advantage of fuzzy rule based systems over other methods is their high transparency. The fuzzy rule based system consists of fuzzy if-then rules such as ?if X1 is large and X2 is medium, then Z is large?. The main problem with the existing fuzzy if-then rules is that as the complexity of the problem increases, the number of rules to define the problem also increases. But this increase in the number of rules is exponential, and not linear. As a result, the memory requirement and the search time also increases. This is the major problem with the earlier systems. Keywords: Genetic Algorithm, Expert System, Fuzzy Expert System, Rule Based System, Efficient Rule Based System

[...] Vol No November 1998. C.Z. Jainikow. A genetic algorithm for optimizing fuzzy decision trees. In Proc. Of 6th International Conf. on Genetic Algorithms, pp. 421-428, July 15- Cordon, O., F. Herrera, et al. Recent advances in genetic fuzzy systems, Information sciences, 136(2001). A. Abraham, Evonf: A framework for optimization of fuzzy inference systems using neural network learning and evolutionary computation, in: The 17th IEEE International Symposium on Intelligent Control,ISIC'02, IEEE Press, ISBN 0780376218, pp.327-332, Canada [10]. [11]. References L. B. [...]


[...] Efficient Rule Base Proposed Genetic Algorithm After creating the rule based system, the next step is to optimize the number of rules. The proposed approach is shown in the fig 2 below in the form of the flowchart: Rule Evaluation Rules / Inferences Fuzzy Output Defuzzification Output Output Membership Function Crisp Output Fig Flowchart for Fuzzy Expert System Generate an initial population, call these as chromosomes Attach fitness value to each of these chromosomes the given initial population. Then the probability is assigned to each of these chromosomes by using the following formula: Select initial population according to fitness value Select ith chromosome for mating with probability Pi = Fitness ? Fitness i = 1 to n Apply crossover operator to chromosomes Xi and Xj Pi = Fitness / ? Fitness i = 1 to n where Xi is the ith chromosome in the given population for I = n Crossover: Here, two or more chromosomes are selected from the solution space depending on their probability. [...]

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