Choosing the Right Algorithm With Hints From Complexity Theory

Abstract

Choosing a suitable algorithm from the myriads of different search heuristics is difficult when faced with a novel optimization problem. In this work, we argue that the purely academic question of what could be the best possible algorithm in a certain broad class of black-box optimizers can give fruitful indications in which direction to search for good established optimization heuristics. We demonstrate this approach on the recently proposed DLB benchmark, for which the only known results are O(n 3 ) runtimes for several classic evolutionary algorithms and an O(n 2 log n) runtime for an estimation-of-distribution