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The search for binary sequences with a high figure of merit, known as the lowautocorrelation binary sequence ($labs$}) problem, represents a formidablecomputational challenge. To mitigate the computational constraints of theproblem, we consider solvers that accept odd values of sequence length $L$ andreturn solutions for skew-symmetric binary sequences only -- with theconsequence that not all best solutions under this constraint will be optimalfor each $L$. In order to improve both, the search for best merit factor $and$the asymptotic runtime performance, we instrumented three stochastic solvers,the first two are state-of-the-art solvers that rely on variants of memetic andtabu search ($lssMAts$ and $lssRRts$), the third solver ($lssOrel$) organizesthe search as a sequence of independent contiguous self-avoiding walk segments.By adapting a rigorous statistical methodology to performance testing of allthree combinatorial solvers, experiments show that the solver with the bestasymptotic average-case performance, $lssOrel\_8 = 0.000032*1.1504^L$, has thebest chance of finding solutions that improve, as $L$ increases, figures ofmerit reported to date. The same methodology can be applied to engineering new$labs$ solvers that may return merit factors even closer to the conjecturedasymptotic value of 12.3248.

Low-Autocorrelation Binary Sequences: On Improved Merit Factors and Runtime Predictions to Achieve Them