Theoretical Analysis of Stochastic Search Algorithms

TL;DR

This paper reviews the mathematical techniques used in the theoretical analysis of stochastic search algorithms, highlighting methods like artificial fitness levels and drift analysis, and applying them to simple evolutionary algorithms.

Contribution

It systematically introduces and explains key mathematical methods for analyzing stochastic search heuristics, including their variants and applications to simple algorithms.

Findings

Analysis techniques like artificial fitness levels and drift are effective for understanding algorithm performance.

The methods are demonstrated on simple evolutionary algorithms with artificial functions.

References are provided for more complex applications and extensions.

Abstract

Theoretical analyses of stochastic search algorithms, albeit few, have always existed since these algorithms became popular. Starting in the nineties a systematic approach to analyse the performance of stochastic search heuristics has been put in place. This quickly increasing basis of results allows, nowadays, the analysis of sophisticated algorithms such as population-based evolutionary algorithms, ant colony optimisation and artificial immune systems. Results are available concerning problems from various domains including classical combinatorial and continuous optimisation, single and multi-objective optimisation, and noisy and dynamic optimisation. This chapter introduces the mathematical techniques that are most commonly used in the runtime analysis of stochastic search heuristics. Careful attention is given to the very popular artificial fitness levels and drift analyses techniques for which several variants are presented. To aid the reader's comprehension of the presented mathematical methods, these are applied to the analysis of simple evolutionary algorithms for artificial example functions. The chapter is concluded by providing references to more complex applications and further extensions of the techniques for the obtainment of advanced results.