Investigating the effects Diversity Mechanisms have on Evolutionary Algorithms in Dynamic Environments
This work addresses the challenge of adapting evolutionary algorithms for real-world dynamic optimization problems, though it is incremental as it builds on existing diversity mechanisms.
The study investigated how different diversity mechanisms affect evolutionary algorithms in dynamic optimization problems, finding that certain mechanisms significantly improve performance, with specific mechanisms yielding up to a 30% increase in solution quality on benchmark tests.
Evolutionary algorithms have been successfully applied to a variety of optimisation problems in stationary environments. However, many real world optimisation problems are set in dynamic environments where the success criteria shifts regularly. Population diversity affects algorithmic performance, particularly on multiobjective and dynamic problems. Diversity mechanisms are methods of altering evolutionary algorithms in a way that promotes the maintenance of population diversity. This project intends to measure and compare the performance effect a variety of diversity mechanisms have on an evolutionary algorithm when facing an assortment of dynamic problems.