SummaryTransmissible cancers are clonal lineages that spread through populations via contagious cancer cells. Although rare in nature, two facial tumor clones affect Tasmanian devils. Here we perform comparative genetic and functional characterization of these lineages. The two cancers have similar patterns of mutation and show no evidence of exposure to exogenous mutagens or viruses. Genes encoding PDGF receptors have copy number gains and are present on extrachromosomal double minutes. Drug screening indicates causative roles for receptor tyrosine kinases and sensitivity to inhibitors of DNA repair. Y chromosome loss from a male clone infecting a female host suggests immunoediting. These results imply that Tasmanian devils may have inherent susceptibility to transmissible cancers and present a suite of therapeutic compounds for use in conservation.

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Algorithm configurators are automated methods to optimise the parameters of an algorithm for a class of problems. We evaluate the performance of a simple random local search configurator (ParamRLS) for tuning the neighbourhood size k of the RLS k algorithm. We measure performance as the expected number of configuration evaluations required to identify the optimal value for the parameter. We analyse the impact of the cutoff time κ (the time spent evaluating a configuration for a problem instance) on the expected number of configuration evaluations required to find the optimal parameter value, where we compare configurations using either best found fitness values (ParamRLS-F) or optimisation times (ParamRLS-T). We consider tuning RLS k for a variant of the R function class (R *), where the performance of each parameter value does not change during the run, and for the O M function class, where longer runs favour smaller k. We rigorously prove that ParamRLS-F efficiently tunes RLS k for R * for any κ while ParamRLS-T requires at least quadratic κ. For O M ParamRLS-F identifies k = 1 as optimal with linear κ while ParamRLS-T requires a κ of at least Ω(n log n). For smaller κ ParamRLS-F identifies that k > 1 performs better while ParamRLS-T returns k chosen uniformly at random.

Recently it has been proved that a simple algorithm configurator called ParamRLS can efficiently identify the optimal neighbourhood size to be used by stochastic local search to optimise two standard benchmark problem classes. In this paper we analyse the performance of algorithm configurators for tuning the more sophisticated global mutation operator used in standard evolutionary algorithms, which flips each of the 𝑛 bits independently with probability 𝜒/𝑛 and the best value for 𝜒 has to be identified. We compare the performance of configurators when the best-found fitness values within the cutoff time 𝜅 are used to compare configurations against the actual optimisation time for two standard benchmark problem classes, Ridge and LeadingOnes. We rigorously prove that all algorithm configurators that use optimisation time as performance metric require cutoff times that are at least as large as the expected optimisation time to identify the optimal configuration. Matters are considerably different if the fitness metric is used. To show this we prove that the simple ParamRLS-F configurator can identify the optimal mutation rates even when using cutoff times that are considerably smaller than the expected optimisation time of the best parameter value for both problem classes. CCS CONCEPTS• Theory of computation → Theory of randomized search heuristics;