A Seft-adaptive Multicellular GEP Algorithm Based On Fuzzy Control For Function Optimization
This work addresses optimization problems in computational intelligence, but it is incremental as it builds on existing GEP methods with fuzzy control enhancements.
The paper tackled improving the global optimization ability of traditional Gene Expression Programming (GEP) by proposing a Multicellular GEP algorithm based on fuzzy control (MGEP-FC), which dynamically adjusts genetic operation rates and enhances population diversity, resulting in significant improvements in stability, global convergence, and optimization speed as shown in 12 benchmark experiments.
To improve the global optimization ability of traditional GEP algorithm, a Multicellular gene expression programming algorithm based on fuzzy control (Multicellular GEP Algorithm Based On Fuzzy Control, MGEP-FC) is proposed. The MGEP-FC algorithm describes the size of cross rate, mutation rate and real number mutation rate by constructing fuzzy membership function. According to the concentration and dispersion of individual fitness values in population, the crossover rate, mutation rate and real number set mutation rate of genetic operation are dynamically adjusted. In order to make the diversity of the population continue in the iterative process, a new genetic operation scheme is designed, which combines the new individuals with the parent population to build a temporary population, and the diversity of the temporary and subpopulation are optimized. The results of 12 Benchmark optimization experiments show that the MGEP-FC algorithm has been greatly improved in stability, global convergence and optimization speed.