# On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms

## TL;DR

This paper demonstrates that populations in standard steady-state genetic algorithms can improve optimization speed for unimodal functions, providing theoretical bounds and novel analytical techniques.

## Contribution

It offers the first non-trivial runtime bounds for the ($$+1) GA on OneMax, showing benefits of population size and introducing new drift analysis methods.

## Key findings

- Expected runtime bounds are lower than unary black box complexity.
- Optimal mutation flips two bits most of the time.
- Runtime decreases with population size up to O(( ext{log} n)).

## Abstract

It is generally accepted that populations are useful for the global exploration of multi-modal optimisation problems. Indeed, several theoretical results are available showing such advantages over single-trajectory search heuristics. In this paper we provide evidence that evolving populations via crossover and mutation may also benefit the optimisation time for hillclimbing unimodal functions. In particular, we prove bounds on the expected runtime of the standard ($\mu$+1)~GA for OneMax that are lower than its unary black box complexity and decrease in the leading constant with the population size up to $\mu=O(\sqrt{\log n})$. Our analysis suggests that the optimal mutation strategy is to flip two bits most of the time. To achieve the results we provide two interesting contributions to the theory of randomised search heuristics: 1) A novel application of drift analysis which compares absorption times of different Markov chains without defining an explicit potential function. 2) The inversion of fundamental matrices to calculate the absorption times of the Markov chains. The latter strategy was previously proposed in the literature but to the best of our knowledge this is the first time is has been used to show non-trivial bounds on expected runtimes.