On the Benefits of Populations on the Exploitation Speed of Standard Steady-State Genetic Algorithms
This provides theoretical evidence for the benefits of populations in evolutionary algorithms, addressing a specific gap in optimization theory for researchers in computational intelligence.
The paper tackles the problem of whether populations can speed up optimization on unimodal functions, proving that the standard (μ+1) GA achieves faster expected runtime on OneMax than unary black box complexity, with runtime decreasing as population size increases up to μ=O(√log n).
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 ($μ$+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 $μ=O(\sqrt{\log n})$. 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.