First Steps Towards a Runtime Comparison of Natural and Artificial Evolution

TL;DR

This paper compares the runtime of natural evolution modeled by SSWM with artificial evolutionary algorithms, revealing conditions where natural evolution can outperform traditional algorithms in crossing fitness valleys and utilizing fitness gradients.

Contribution

It provides the first runtime analysis of the SSWM natural evolution model and compares its performance with the (1+1)EA, highlighting potential advantages in specific scenarios.

Findings

SSWM can outperform (1+1)EA in crossing fitness valleys

Natural evolution benefits from fitness gradient information

SSWM shows moderate advantage over (1+1)EA in certain conditions

Abstract

Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse their runtime on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrence of new mutations is much longer than the time it takes for a new beneficial mutation to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a (1+1)-type process where the probability of accepting a new genotype (improvements or worsenings) depends on the change in fitness. We present an initial runtime analysis of SSWM, quantifying its performance for various parameters and investigating differences to the (1+1)EA. We show that SSWM can have a moderate advantage over the (1+1)EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1)EA by taking advantage of information on the fitness gradient.