Derivation of Upper Bounds on Optimization Time of Population-Based Evolutionary Algorithm on a Function with Fitness Plateaus Using Elitism Levels Traverse Mechanism
This work addresses theoretical analysis challenges in evolutionary computation for researchers, but it is incremental as it applies an existing tool to a specific test function.
The authors tackled the problem of analyzing optimization time for population-based evolutionary algorithms on functions with fitness plateaus, specifically the Royal Roads problem, and derived asymptotic upper bounds while approximating the limiting distribution of a subset of the population.
In this article a tool for the analysis of population-based EAs is used to derive asymptotic upper bounds on the optimization time of the algorithm solving Royal Roads problem, a test function with plateaus of fitness. In addition to this, limiting distribution of a certain subset of the population is approximated.