Optimal Parameter Choices via Precise Black-Box Analysis
This work provides incremental improvements in precise runtime analysis for evolutionary algorithms, benefiting researchers in computational complexity and optimization.
The paper tackles the problem of understanding evolutionary algorithm parameters through precise runtime analysis, proving that the unary unbiased black-box complexity of the OneMax function is n ln(n) - cn ± o(n) with c between 0.2539 and 0.2665, and shows their algorithm finds solutions roughly 13% closer to the optimum than previous methods.
It has been observed that some working principles of evolutionary algorithms, in particular, the influence of the parameters, cannot be understood from results on the asymptotic order of the runtime, but only from more precise results. In this work, we complement the emerging topic of precise runtime analysis with a first precise complexity theoretic result. Our vision is that the interplay between algorithm analysis and complexity theory becomes a fruitful tool also for analyses more precise than asymptotic orders of magnitude. As particular result, we prove that the unary unbiased black-box complexity of the OneMax benchmark function class is $n \ln(n) - cn \pm o(n)$ for a constant $c$ which is between $0.2539$ and $0.2665$. This runtime can be achieved with a simple (1+1)-type algorithm using a fitness-dependent mutation strength. When translated into the fixed-budget perspective, our algorithm finds solutions which are roughly 13\% closer to the optimum than those of the best previously known algorithms. To prove our results, we formulate several new versions of the variable drift theorems, which also might be of independent interest.