First Steps Towards a Runtime Comparison of Natural and Artificial Evolution
This work provides an incremental theoretical analysis for evolutionary computation researchers, bridging natural and artificial evolution models.
The paper tackled the problem of comparing the runtime performance of natural evolution models, specifically the Strong Selection Weak Mutation (SSWM) regime, to artificial evolutionary algorithms like the (1+1)EA, showing that SSWM can have a moderate advantage in crossing fitness valleys and outperforms the (1+1)EA in some cases by leveraging fitness gradient information.
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.