Error Analysis of Elitist Randomized Search Heuristics

TL;DR

This paper introduces an error analysis framework for randomized search heuristics that estimates expected approximation errors, providing a more flexible and precise alternative to traditional hitting time analysis, especially for complex optimization problems.

Contribution

It proposes a novel error analysis method based on Markov chain models to evaluate the performance of randomized search heuristics beyond asymptotic fitness results.

Findings

Works well for uni- and multi-modal benchmark problems

Provides precise approximation error estimates

Offers a flexible alternative to hitting time analysis

Abstract

When globally optimal solutions of complicated optimization problems cannot be located by evolutionary algorithms (EAs) in polynomial expected running time, the hitting time/running time analysis is not flexible enough to accommodate the requirement of theoretical study, because sometimes we have no idea on what approximation ratio is available in polynomial expected running time. Thus, it is necessary to propose an alternative routine for the theoretical analysis of EAs. To bridge the gap between theoretical analysis and algorithm implementation, in this paper we perform an error analysis where expected approximation error is estimated to evaluate performances of randomized search heuristics (RSHs). Based on the Markov chain model of RSHs, the multi-step transition matrix can be computed by diagonalizing the one-step transition matrix, and a general framework for estimation of expected approximation errors is proposed. Case studies indicate that the error analysis works well for both uni- and multi-modal benchmark problems. It leads to precise estimations of approximation error instead of asymptotic results on fitness values, which demonstrates its competitiveness to fixed budget analysis.