Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
IEEE Expert: Intelligent Systems and Their Applications
Evolving Compact Solutions in Genetic Programming: A Case Study
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Pattern Recognition Letters
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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Genetic Programming can be used to evolve Fuzzy Rule-based classifiers [7]. Fuzzy GP depends on a grammar defining valid expressions of fuzzy classifiers, and guarantees that all individuals in the population are valid instances of it all along the evolution process. This is accomplished by restricting crossover and mutation so that they only take place at points of the derivation tree representing the same non-terminal, thus generating valid subtrees [13]. In Fuzzy GP, terminal symbols are fuzzy constants and variables that are chosen beforehand. In this work we propose a method for evolving both fuzzy membership functions of the variables and the Rule Base. Our method extends the GA-P hybrid method [6] by introducing a new grammar with two functional parts, one for the Fuzzy Rule Base (GP Part), and the other for the constants that define the shapes of the fuzzy sets involved in the Fuzzy Rule Base (GA Part). We have applied this method to some classical benchmarks taken from the collection of test data at the UCI Repository of Machine Learning Databases [9].