A Reinforced Evolution-Based Approach to Multi-Resource Load Balancing
This work addresses load balancing in multi-resource systems, but appears incremental as it builds on standard genetic methods with specific adaptations.
The paper tackled the multi-resource load balancing problem by introducing a reinforced genetic approach with modifications like a migration operator to overcome strict feasibility constraints, achieving unspecified improvements.
This paper presents a reinforced genetic approach to a defined d-resource system optimization problem. The classical evolution schema was ineffective due to a very strict feasibility function in the studied problem. Hence, the presented strategy has introduced several modifications and adaptations to standard genetic routines, e.g.: a migration operator which is an analogy to the biological random genetic drift.