Elitism Levels Traverse Mechanism For The Derivation of Upper Bounds on Unimodal Functions
This provides theoretical guarantees for evolutionary algorithm performance on unimodal functions, which is incremental for the optimization community.
The authors tackled the problem of deriving upper bounds for population-based evolutionary algorithms on unimodal functions by designing an Elitism Levels Traverse Mechanism, proving its efficiency theoretically and demonstrating bounds of cμn log n - O(μ n) on the OneMax function.
In this article we present an Elitism Levels Traverse Mechanism that we designed to find bounds on population-based Evolutionary algorithms solving unimodal functions. We prove its efficiency theoretically and test it on OneMax function deriving bounds cμn log n - O(μ n). This analysis can be generalized to any similar algorithm using variants of tournament selection and genetic operators that flip or swap only 1 bit in each string.