Hasil untuk "Evolution"

Jing Zhu, Liangsheng Hu, Pengxiang Zhao et al.

Hydrogen fuel is considered as the cleanest renewable resource and the primary alternative to fossil fuels for future energy supply. Sustainable hydrogen generation is the major prerequisite to realize future hydrogen economy. The electrocatalytic hydrogen evolution reaction (HER), as the vital step of water electrolysis to H2 production, has been the subject of extensive study over the past decades. In this comprehensive review, we first summarize the fundamentals of HER and review the recent state-of-the-art advances in the low-cost and high-performance catalysts based on noble and non-noble metals, as well as metal-free HER electrocatalysts. We systemically discuss the insights into the relationship among the catalytic activity, morphology, structure, composition, and synthetic method. Strategies for developing an effective catalyst, including increasing the intrinsic activity of active sites and/or increasing the number of active sites, are summarized and highlighted. Finally, the challenges, perspectives, and research directions of HER electrocatalysis are featured.

Tim Salimans, Jonathan Ho, Xi Chen et al.

We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients. Experiments on MuJoCo and Atari show that ES is a viable solution strategy that scales extremely well with the number of CPUs available: By using a novel communication strategy based on common random numbers, our ES implementation only needs to communicate scalars, making it possible to scale to over a thousand parallel workers. This allows us to solve 3D humanoid walking in 10 minutes and obtain competitive results on most Atari games after one hour of training. In addition, we highlight several advantages of ES as a black box optimization technique: it is invariant to action frequency and delayed rewards, tolerant of extremely long horizons, and does not need temporal discounting or value function approximation.

Esteban Real, Sherry Moore, Andrew Selle et al.

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Our goal is to minimize human participation, so we employ evolutionary algorithms to discover such networks automatically. Despite significant computational requirements, we show that it is now possible to evolve models with accuracies within the range of those published in the last year. Specifically, we employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions and reaching accuracies of 94.6% (95.6% for ensemble) and 77.0%, respectively. To do this, we use novel and intuitive mutation operators that navigate large search spaces; we stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements.

N. Mcgranahan, C. Swanton

Fang Li

N. Hansen

This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.

Hong Li, Qing Zhang, C. C. Yap et al.

C. B. Lowe, M. Kellis, Adam Siepel et al.

C. Ponting, P. Oliver, W. Reik

R. Ley, M. Hamady, C. Lozupone et al.

A. Şengör, Y. Yılmaz

W. Hamilton

John Maynard Smith, E. Szathmáry

P. Kay

G. Horridge

J. Davies, D. Davies

C. Woese

M. Nei, Sudhir Kumar

G. Williams

A new individual entering a population may be said to have a reproductive probability distribution. The reproductive probability is zero from zygote to reproductive maturity. Later, perhaps shortly after maturity, it reaches a peak value. Then it declines because of the cumulative probability of death. There is a cumulative probability of death with or without senescence. The selective value of a gene depends on how it affects the total reproductive probability. Selection of a gene that confers an advantage at one age and a disadvantage at another will depend not only on the magnitudes of the effects themselves but also on the times of the effects. An advantage during the period of maximum reproductive probability would increase the total reproductive probability more than a proportionately similar disadvantage later on would decrease it. So natural selection will frequently maximize vigor in youth at the expense of vigor later on and thereby produce a declining vigor (senescence) during adult life. Selection, of course, will act to minimize the rate of this decline whenever possible. The rate of senescence shown by any species will reflect the balance between this direct adverse selection of senescence as an unfavorable character and the indirect favorable selection through the age-related bias in the selection of pleiotropic genes. Variations in the amount of fecundity increase after maturity, in the adult mortality rate, and in other life history features would affect the shape of the reproductive probability distribution and thereby influence the evolution of senescence. Any factor that decreases the rate of decline in reproductive probability intensifies selection against senescence. Any factor that increases the rate of this decline causes a relaxed selection against senescence and a greater advantage in increasing youthful vigor at the price of vigor later on. These considerations explain much of what is known of phylogenetic variation in rates of senescence. Other deductions from the theory are also supported by limited available evidence. These include the expectation that rapid morphogenesis should be associated with rapid senescence, that senescence should always be a generalized deterioration of many organs and systems, and that postreproductive periods be short and infrequent in any wild population. Reproduced by permission. G. C. Williams, Pleiotropy, Natural Selection, and the Evolution of Senescence. Evolution 11 , 398-411 (1957).

P. Nowell