Title: Towards Grammatical Evolution-Based Automated Design of Differential Evolution Algorithm
Abstract: Differential evolution (DE) is a robust evolutionary algorithm that has been applied for various real world optimization problems. However, the performance of DE depends on the optimal choice of variation operators and control parameters. The dizzying choice of heuristics for choosing mutation strategies, crossover operator and control parameters makes DE design a challenging task for a practitioner. We present a meta-evolutionary approach with grammatical evolution (GE) to evolve effective parameter configurations for classical differential evolution algorithm. It has been observed that the GE evolved DE configurations performed competitively on the chosen ten standard benchmark functions. This work is a preliminary step towards automating DE algorithm designs, which has the potential to relieve a user from the painful task of trial-and-error manual designs.
Publication Year: 2021
Publication Date: 2021-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 4
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot