Analysis of Noisy Evolutionary Optimization When Sampling Fails

In noisy evolutionary optimization, sampling is a common strategy to deal with noise. By the sampling strategy, the fitness of a solution is evaluated multiple times (called \emph{sample size}) independently, and its true fitness is then approximated by the average of these evaluations. Previous studies on sampling are mainly empirical. In this paper, we first investigate the effect of sample size from a theoretical perspective. By analyzing the (1+1)-EA on the noisy LeadingOnes problem, we show that as the sample size increases, the running time can reduce from exponential to polynomial, but then return to exponential. This suggests that a proper sample size is crucial in practice. Then, we investigate what strategies can work when sampling with any fixed sample size fails. By two illustrative examples, we prove that using parent or offspring populations can be better. Finally, we construct an artificial noisy example to show that when using neither sampling nor populations is effective, adaptive sampling (i.e., sampling with an adaptive sample size) can work. This, for the first time, provides a theoretical support for the use of adaptive sampling

On the Robustness of Median Sampling in Noisy Evolutionary Optimization

In real-world optimization tasks, the objective (i.e., fitness) function evaluation is often disturbed by noise due to a wide range of uncertainties. Evolutionary algorithms (EAs) have been widely applied to tackle noisy optimization, where reducing the negative effect of noise is a crucial issue. One popular strategy to cope with noise is sampling, which evaluates the fitness multiple times and uses the sample average to approximate the true fitness. In this paper, we introduce median sampling as a noise handling strategy into EAs, which uses the median of the multiple evaluations to approximate the true fitness instead of the mean. We theoretically show that median sampling can reduce the expected running time of EAs from exponential to polynomial by considering the (1+1)-EA on OneMax under the commonly used one-bit noise. We also compare mean sampling with median sampling by considering two specific noise models, suggesting that when the 2-quantile of the noisy fitness increases with the true fitness, median sampling can be a better choice. The results provide us with some guidance to employ median sampling efficiently in practice.Comment: 19 pages. arXiv admin note: text overlap with arXiv:1810.05045, arXiv:1711.0095

Running Time Analysis of the (1+1)-EA for Robust Linear Optimization

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint $k$. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter $d$ or budget $k$ allowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization.Comment: 17 pages, 1 tabl

The Asian arowana (Scleropages formosus) genome provides new insights into the evolution of an early lineage of teleosts

The Asian arowana (Scleropages formosus), one of the world’s most expensive cultivated ornamental fishes, is an endangered species. It represents an ancient lineage of teleosts: the Osteoglossomorpha. Here, we provide a high-quality chromosome-level reference genome of a female golden-variety arowana using a combination of deep shotgun sequencing and high-resolution linkage mapping. In addition, we have also generated two draft genome assemblies for the red and green varieties. Phylogenomic analysis supports a sister group relationship between Osteoglossomorpha (bonytongues) and Elopomorpha (eels and relatives), with the two clades together forming a sister group of Clupeocephala which includes all the remaining teleosts. The arowana genome retains the full complement of eight Hox clusters unlike the African butterfly fish (Pantodon buchholzi), another bonytongue fish, which possess only five Hox clusters. Differential gene expression among three varieties provides insights into the genetic basis of colour variation. A potential heterogametic sex chromosome is identified in the female arowana karyotype, suggesting that the sex is determined by a ZW/ZZ sex chromosomal system. The high-quality reference genome of the golden arowana and the draft assemblies of the red and green varieties are valuable resources for understanding the biology, adaptation and behaviour of Asian arowanas

DurIAN-E: Duration Informed Attention Network For Expressive Text-to-Speech Synthesis

This paper introduces an improved duration informed attention neural network (DurIAN-E) for expressive and high-fidelity text-to-speech (TTS) synthesis. Inherited from the original DurIAN model, an auto-regressive model structure in which the alignments between the input linguistic information and the output acoustic features are inferred from a duration model is adopted. Meanwhile the proposed DurIAN-E utilizes multiple stacked SwishRNN-based Transformer blocks as linguistic encoders. Style-Adaptive Instance Normalization (SAIN) layers are exploited into frame-level encoders to improve the modeling ability of expressiveness. A denoiser incorporating both denoising diffusion probabilistic model (DDPM) for mel-spectrograms and SAIN modules is conducted to further improve the synthetic speech quality and expressiveness. Experimental results prove that the proposed expressive TTS model in this paper can achieve better performance than the state-of-the-art approaches in both subjective mean opinion score (MOS) and preference tests

Effects of whole maize high-grain diet feeding on colonic fermentation and bacterial community in weaned lambs

High-grain diet is commonly used in intensive production to boost yield in short term, which may cause adverse effects such as rumen and colonic acidosis in ruminants. Maize is one of the key components of high-grain diet, and different processing methods of maize affect the digestive absorption and gastrointestinal development of ruminants. To investigate the effects of maize form in high-grain diets on colonic fermentation and bacterial community of weaned lambs, twenty-two 2.5-month-old healthy Hu lambs were fed separately a maize meal low-grain diet (19.2% grain; CON), a maize meal high-grain diet (50.4% grain; CM), and a whole maize high-grain diet (50.4% grain; CG). After 7 weeks of feeding, the total volatile fatty acid concentration (P = 0.035) were significantly higher in lambs from CM than that from CON. The sequencing results of colonic content microbial composition revealed that the relative abundance of genera Parasutterella (P = 0.028), Comamonas (P = 0.031), Butyricicoccus (P = 0.049), and Olsenella (P = 0.010) were higher in CM than those in CON; compared with CM, the CG diet had the higher relative abundance of genera Bacteroides (P = 0.024) and Angelakisella (P = 0.020), while the lower relative abundance of genera Olsenella (P = 0.031) and Paraprevotella (P = 0.006). For colonic mucosal microbiota, the relative abundance of genera Duncaniella (P = 0.024), Succiniclasticum (P = 0.044), and Comamonas (P = 0.012) were significantly higher in CM than those in CON. In comparison, the relative abundance of genera Alistipes (P = 0.020) and Campylobacter (P = 0.017) were significantly lower. And the relative abundance of genera Colidextribacter (P = 0.005), Duncaniella (P = 0.032), Christensenella (P = 0.042), and Lawsonibacter (P = 0.018) were increased in the CG than those in the CM. Furthermore, the CG downregulated the relative abundance of genes encoding infectious-disease-parasitic (P = 0.049), cancer-specific-types (P = 0.049), and neurodegenerative-disease (P = 0.037) in colonic microbiota than those in the CM. Overall, these results indicated that maize with different grain sizes might influence the colonic health of weaned lambs by altering the composition of the colonic bacterial community

Self-sustainable protonic ceramic electrochemical cells using a triple conducting electrode for hydrogen and power production.

The protonic ceramic electrochemical cell (PCEC) is an emerging and attractive technology that converts energy between power and hydrogen using solid oxide proton conductors at intermediate temperatures. To achieve efficient electrochemical hydrogen and power production with stable operation, highly robust and durable electrodes are urgently desired to facilitate water oxidation and oxygen reduction reactions, which are the critical steps for both electrolysis and fuel cell operation, especially at reduced temperatures. In this study, a triple conducting oxide of PrNi0.5Co0.5O3-δ perovskite is developed as an oxygen electrode, presenting superior electrochemical performance at 400~600 °C. More importantly, the self-sustainable and reversible operation is successfully demonstrated by converting the generated hydrogen in electrolysis mode to electricity without any hydrogen addition. The excellent electrocatalytic activity is attributed to the considerable proton conduction, as confirmed by hydrogen permeation experiment, remarkable hydration behavior and computations