RDEx-SOP: Exploitation-Biased Reconstructed Differential Evolution for Fixed-Budget Bound-Constrained Single-Objective Optimization

1. 1Combine success-history parameter adaptation with an exploitation-biased hybrid branch
2. 2Implement lightweight reconstruction to improve the algorithm's efficiency
3. 3Apply RDEx-SOP to bound-constrained single-objective numerical optimization problems
4. 4Evaluate the performance of RDEx-SOP using benchmark functions and compare with other state-of-the-art algorithms

This research benefits AI-engineers and ml-researchers working on optimization problems, as it provides a new approach to improve the efficiency and robustness of evolutionary algorithms

💡 RDEx-SOP combines success-history parameter adaptation and exploitation-biased hybrid branch to improve optimization efficiency

💡 New algo: RDEx-SOP for single-objective optimization!