Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy

This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012

Black-box optimization benchmarking of IPOP-saACM-ES on the BBOB-2012 noisy testbed

In this paper, we study the performance of IPOP-saACM-ES, recently proposed self-adaptive surrogate-assisted Covariance Matrix Adaptation Evolution Strategy. The algorithm was tested using restarts till a total number of function evaluations of $10^6D$ was reached, where $D$ is the dimension of the function search space. The experiments show that the surrogate model control allows IPOP-saACM-ES to be as robust as the original IPOP-aCMA-ES and outperforms the latter by a factor from 2 to 3 on 6 benchmark problems with moderate noise. On 15 out of 30 benchmark problems in dimension 20, IPOP-saACM-ES exceeds the records observed during BBOB-2009 and BBOB-2010.Comment: Genetic and Evolutionary Computation Conference (GECCO 2012) (2012

KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization

This paper investigates the control of an ML component within the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box optimization. The known CMA-ES weakness is its sample complexity, the number of evaluations of the objective function needed to approximate the global optimum. This weakness is commonly addressed through surrogate optimization, learning an estimate of the objective function a.k.a. surrogate model, and replacing most evaluations of the true objective function with the (inexpensive) evaluation of the surrogate model. This paper presents a principled control of the learning schedule (when to relearn the surrogate model), based on the Kullback-Leibler divergence of the current search distribution and the training distribution of the former surrogate model. The experimental validation of the proposed approach shows significant performance gains on a comprehensive set of ill-conditioned benchmark problems, compared to the best state of the art including the quasi-Newton high-precision BFGS method

«Mezh tem vverhu zvezda siyaet…» [“Meanwhile, the top star shines”…]: Zabolotsky and Mirovedenie

The article was submitted on 20.01.2015.Статья посвящена теме соприкосновения поэтического мира Николая Заболоцкого с деятельностью Русского общества любителей мироведения (Р.О.Л.М.), работавшего в Санкт-Петербурге - Петрограде - Ленинграде с 1909 по 1932 г. В 1928 г. в журнале «Мироведение» была напечатана статья астронома и историка науки Д. О. Святского «Сказание о Чигирь-звезде и телескопические наблюдения Галилея (Из истории астрономии в России)». Она была посвящена переводным русским астрономическим и астрологическим компиляциям XVI-XVII столетий («Сказание царя Соломона, что есть печать большая, откуду, как ему приде» и других), где говорилось о таинственной звезде Чигирь. Поэма Заболоцкого «Безумный волк», написанная в 1931 г., восходит не только к его знакомству с учением К. Э. Циолковского, но и к впечатлениям от чтения этой статьи, а также содержит следы чтения сочинений историков, этнографов и фольклористов, упоминавших о Чигирь-звезде (И. П. Сахаров, А. Н. Афанасьев, А. И. Соболевский, А. С. Ермолов, В. Н. Перетс). Другие упоминания звезд, созвездий, планет, телескопов и астрономов у Заболоцкого (и раннего, и позднего периодов), вероятно, также связаны с памятью о Р.О.Л.М., где прообраз синтетической и всемогущей науки будущего виделся в оккультной «древней науке» и «народной астрономии».The article considers the correspondence between the poetic world of Nikolai Zabolotsky and the activity of the Russian Society of Amateurs of Natural Sciences that worked in Saint Petersburg - Petrograd - Leningrad between 1909 and 1932. In 1928, in the Mirovedenie Journal (Russian for Natural Sciences) astronomer and science historian D. O. Svyatsky published an article entitled A Tale of Star Chigir and the Telescopic Observations of Galileo (From the History of Astronomy in Russia). It considered the Russian translations of the astronomical and astrological compiled works of the 16th and 17th centuries (A Tale of King Solomon of What Is Great Sorrow and Wherefrom It Shall Come and others) which related to the mysterious Star Chigir. Zabolotsky’s poem The Mad Wolf (1931) was not only inspired by the poet’s meeting with K. E. Tsiolkovsky but also his impressions following his reading of the abovementioned article and has data proving his acquaintance with works of historians, ethnographers and folklorists that mentioned Star Chigir (I. P. Sakharov, A. N. Afanasyev, A. I. Sobolevsky, A. S. Yermolov, V. N. Perets). Other instances of Zabolotsky’s mentioning of stars, constellations, planets, telescopes and astronomers (both during the early and late periods of his work) are most likely connected with the Russian Society of Amateurs of Natural Sciences which considered the synthetic and almighty science of the future as rooted in the occult science of the old times and popular astronomy