Black-Box Complexity: Breaking the O(nlog⁡n)O(n \log n) Barrier of LeadingOnes

We show that the unrestricted black-box complexity of the $n$-dimensional XOR- and permutation-invariant LeadingOnes function class is $O(n \log (n) / \log \log n)$. This shows that the recent natural looking $O(n\log n)$ bound is not tight. The black-box optimization algorithm leading to this bound can be implemented in a way that only 3-ary unbiased variation operators are used. Hence our bound is also valid for the unbiased black-box complexity recently introduced by Lehre and Witt (GECCO 2010). The bound also remains valid if we impose the additional restriction that the black-box algorithm does not have access to the objective values but only to their relative order (ranking-based black-box complexity).Comment: 12 pages, to appear in the Proc. of Artificial Evolution 2011, LNCS 7401, Springer, 2012. For the unrestricted black-box complexity of LeadingOnes there is now a tight $\Theta(n \log\log n)$ bound, cf. http://eccc.hpi-web.de/report/2012/087

Enhancement of Opioid-Mediated Analgesia\ud by Ingestion of Amniotic Fluid:\ud Onset Latency and Duration

Ingestion of placenta and amniotic fluid has been shown to enhance opioid-mediated analgesia produced by morphine injection, footshock, vaginal/cervical stimulation, and during late pregnancy in rats. The present study was designed to determine how soon after ingestion the enhancement begins and how long it lasts. Tail-flick latencies in Long-Evans rats were determined before and during vaginal/cervical stimulation; analgesia was measured as the percent increase in tail-flick latency during vaginal stimulation. After determination of baseline, rats were intubated with 0.25 ml of either amniotic fluid or beef bouillon. We found that analgesia enhancement was detectable as early as 5 minutes after ingestion of amniotic fluid, and the effect lasted at least 30 minutes, but no longer than 40 minutes

Amniotic-Fluid Ingestion Enhances\ud Morphine Analgesia During Morphine\ud Tolerance and Withdrawal in Rats

Ingestion of placenta and amniotic fluid has been shown to enhance opioid-mediated analgesia in rats produced by morphine injection. footshock, vaginal/cervical stimulation, and during late pregnancy. The present study was designed to investigate the effects of amniotic fluid ingestion on the characteristics of morphine dependency and withdrawal. Tail-flick latencies in Long-Evans rats were determined before and after repeated daily injections of morphine sulfate. It was found that ingestion of amniotic fluid after establishment of the morphine dependency, coupled with an injection of an otherwise ineffective dose of morphine, enhanced analgesia in morphine-dependent rats, and reversed hyperalgesia seen during withdrawal from morphine dependency

Runtime Analysis of a Heavy-Tailed (1+(λ,λ))(1+(\lambda,\lambda)) Genetic Algorithm on Jump Functions

It was recently observed that the $(1+(\lambda,\lambda))$ genetic algorithm can comparably easily escape the local optimum of the jump functions benchmark. Consequently, this algorithm can optimize the jump function with jump size $k$ in an expected runtime of only $n^{(k + 1)/2}k^{-k/2}e^{O(k)}$ fitness evaluations (Antipov, Doerr, Karavaev (GECCO 2020)). To obtain this performance, however, a non-standard parameter setting depending on the jump size $k$ was used. To overcome this difficulty, we propose to choose two parameters of the $(1+(\lambda,\lambda))$ genetic algorithm randomly from a power-law distribution. Via a mathematical runtime analysis, we show that this algorithm with natural instance-independent choices of the distribution parameters on all jump functions with jump size at most $n/4$ has a performance close to what the best instance-specific parameters in the previous work obtained. This price for instance-independence can be made as small as an $O(n\log(n))$ factor. Given the difficulty of the jump problem and the runtime losses from using mildly suboptimal fixed parameters (also discussed in this work), this appears to be a fair price.Comment: An extended version of the same-titled paper from PPSN 202

Ingested bovine amniotic fluid enhances morphine antinociception in rats

Ingestion by rats of rat placenta or amniotic fluid enhances opioid-mediated, or partly opioid-mediated, antinociception produced by morphine injection, vaginal or cervical stimulation, late pregnancy, and foot shock. This phenomenon is believed to be produced by a placental\ud opioid-enhancing factor (POEF). Ingestion by rats of human or dolphin placenta has also been shown to enhance opioid antinociception, suggesting that POEF may be common to many mammalian species. We tested bovine amniotic fluid (BAF) for its capacity to enhance morphine antinociception in female Long-Evans rats, as determined by percentage change from baseline tail-flick latency in response to radiant heat, and we report that 0.50 mL BAF effectively enhanced morphine antinociception but did not by itself produce antinociception. The efficacy of POEF across species suggests that POEF may have been functionally (and structurally) conserved during evolution. Furthermore, the availability of POEF at parturition, as well as its ability to enhance pregnancy-mediated antinociception without\ud disrupting maternal behavior, offers a tenable explanation for the long-debated ultimate causality of placentophagia

Searching for Radio Pulsars in 3EG Sources at Urumqi Observatory

Since mid-2005, a pulsar searching system has been operating at 18 cm on the 25-m radio telescope of Urumqi Observatory. Test observations on known pulsars show that the system can perform the intended task. The prospect of using this system to observe 3EG sources and other target searching tasks is discussed.Comment: a training project about MSc thesi

Hybridizing the 1/5-th Success Rule with Q-Learning for Controlling the Mutation Rate of an Evolutionary Algorithm

It is well known that evolutionary algorithms (EAs) achieve peak performance only when their parameters are suitably tuned to the given problem. Even more, it is known that the best parameter values can change during the optimization process. Parameter control mechanisms are techniques developed to identify and to track these values. Recently, a series of rigorous theoretical works confirmed the superiority of several parameter control techniques over EAs with best possible static parameters. Among these results are examples for controlling the mutation rate of the $(1+\lambda)$~EA when optimizing the OneMax problem. However, it was shown in [Rodionova et al., GECCO'19] that the quality of these techniques strongly depends on the offspring population size $\lambda$. We introduce in this work a new hybrid parameter control technique, which combines the well-known one-fifth success rule with Q-learning. We demonstrate that our HQL mechanism achieves equal or superior performance to all techniques tested in [Rodionova et al., GECCO'19] and this -- in contrast to previous parameter control methods -- simultaneously for all offspring population sizes $\lambda$. We also show that the promising performance of HQL is not restricted to OneMax, but extends to several other benchmark problems.Comment: To appear in the Proceedings of Parallel Problem Solving from Nature (PPSN'2020

Evolutionary Algorithms with Self-adjusting Asymmetric Mutation

Evolutionary Algorithms (EAs) and other randomized search heuristics are often considered as unbiased algorithms that are invariant with respect to different transformations of the underlying search space. However, if a certain amount of domain knowledge is available the use of biased search operators in EAs becomes viable. We consider a simple (1+1) EA for binary search spaces and analyze an asymmetric mutation operator that can treat zero- and one-bits differently. This operator extends previous work by Jansen and Sudholt (ECJ 18(1), 2010) by allowing the operator asymmetry to vary according to the success rate of the algorithm. Using a self-adjusting scheme that learns an appropriate degree of asymmetry, we show improved runtime results on the class of functions OneMax$_a$ describing the number of matching bits with a fixed target $a\in\{0,1\}^n$.Comment: 16 pages. An extended abstract of this paper will be published in the proceedings of PPSN 202

First Steps Towards a Runtime Analysis When Starting with a Good Solution

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