Bayesian optimization using sequential Monte Carlo

We consider the problem of optimizing a real-valued continuous function $f$ using a Bayesian approach, where the evaluations of $f$ are chosen sequentially by combining prior information about $f$, which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are used to choose evaluation points. In this article, we decide to use a Sequential Monte Carlo (SMC) approach

An Experimental Evaluation of Bayesian Optimization on Bipedal Locomotion

© 2014 IEEE.The design of gaits and corresponding control policies for bipedal walkers is a key challenge in robot locomotion. Even when a viable controller parametrization already exists, finding near-optimal parameters can be daunting. The use of automatic gait optimization methods greatly reduces the need for human expertise and time-consuming design processes. Many different approaches to automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this paper, we present some common methods for automatic gait optimization in bipedal locomotion, and analyze their strengths and weaknesses. We experimentally evaluated these gait optimization methods on a bipedal robot, in more than 1800 experimental evaluations. In particular, we analyzed Bayesian optimization in different configurations, including various acquisition functions

Bayesian Optimization for Adaptive MCMC

This paper proposes a new randomized strategy for adaptive MCMC using Bayesian optimization. This approach applies to non-differentiable objective functions and trades off exploration and exploitation to reduce the number of potentially costly objective function evaluations. We demonstrate the strategy in the complex setting of sampling from constrained, discrete and densely connected probabilistic graphical models where, for each variation of the problem, one needs to adjust the parameters of the proposal mechanism automatically to ensure efficient mixing of the Markov chains.Comment: This paper contains 12 pages and 6 figures. A similar version of this paper has been submitted to AISTATS 2012 and is currently under revie

Response surface applied to mixtures of castor bean hull and presscake for organic fertilization of castor bean plants

Castor bean presscake and hull are the two sub-products resulting from the extraction of castor bean oil. Their use as organic fertilizers has aroused interest of producers who wish to aggregate value to these products and use them rationally. The growth of castor bean plants of the BRS Energia cultivar in substrates composed of mixtures of two organic materials (castor bean presscake and hull) associated to soil was evaluated in this paper. The experiment was carried out at the Embrapa Algodão greenhouse, in the city of Campina Grande, in Paraíba, Brazil, from February 2010 to June 2010. Five treatments with four replications were evaluated. The treatments were composed of soil, varying from 80 to 100%, and castor bean hull and presscake varying from 0 to 10%. In order to identify the best mixture to promote growth of the plants, the extreme-vertices experimental design was used in a simplex sub-region, due to the restrictions of some components of the mixture. The classification of the best mixture was made based on the following variables: plant height, leaf area, and total dry weight of the plant. These measurements were evaluated by mixture modeling, performed in the MATLAB® computer system. For all of the analyzed variables, the best mixture was composed of 10% of castor bean presscake, 10% of castor bean hull, and 80% of soil

Bayesian Optimization for Learning Gaits under Uncertainty

© 2015, Springer International Publishing Switzerland.Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date. However, no extensive comparison among them has yet been performed. In this article, we thoroughly discuss multiple automatic optimization methods in the context of gait optimization. We extensively evaluate Bayesian optimization, a model-based approach to black-box optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments

Optimasi Proses Pembuatan Serbuk Lobak dengan Metode Foam Mat Drying

Lobak merupakan bahan yang potensial untuk dikembangkan. Hal ini dapat dilihat dari produksi yang terus meningkat dari tahun ke tahun dan kandungan senyawa didalamnya yang sangat bermanfaat untuk tubuh. Akan tetapi, tidak diimbangi dengan pemanfaatan secara maksimal karena teknologi untuk pengolahannya masih sederhana. Terdapat kandungan senyawa kimia pada lobak yang bermanfaat yaitu polifenol. Perannya adalah untuk mengurangi kadar asam urat dengan cara penghambatan kerja xantin oksidase. Penyakit artritis gout atau asam urat adalah salah satu penyakit inflamasi sendi yang ditandai dengan penumpukan kristal monosodium urat di dalam ataupun di sekitar persendian. Lobak harus disajikan lebih sederhana agar mudah dikonsumsi, yaitu dengan cara dijadikan serbuk. Pembuatan serbuk dilakukan dengan metode Foam Mat Drying, keuntungannya adalah suhu lebih rendah kualitas rasa, warna dan kandungan produk nutrisi produk akhir yang lebih baik. Pada proses pembuatan serbuk, proses pemanasan dan penambahan bahan pengisi seperti maltodekstrin dapat berpengaruh terhadap kandungan senyawa aktif pada lobak. Oleh karena itu perlu dilakukan optimasi pada faktor tersebut. Adapun metode optimasi proses pembubukan yang digunakan adalah Metode Permukaan Tanggap (RSM) dengan rancangan komposit terpusat faktorial 22. Pada optimasi ini terdapat dua faktor yaitu suhu pengeringan (X1) yaitu 50, 60 dan 70° C, serta rasio maltodekstrin (X2) yaitu 6, 8 dan 10% b/b. Pada keduanya terbentuk kode (-1.414, -1, 0, +1, +1.414) dimana nilai -1 sebagai nilai minimal, nilai 0 sebagai nilai tengah dan nilai +1 sebagai nilai maksimal dari faktor. Berdasarkan penelitian, didapatkan komposisi formula terbaik yakni suhu sebesar 58.49 °C, dan konsentrasi maltodekstrin sebesar 7.31%. Formula optimal tersebut diprediksikan mendapatkan nilai total fenol sebesar 7.69 %, aktivitas antioksidan sebesar 37.6631 mg/ml per 100 mg, dan rendemen sebesar 8.73 %. Total fenol yang didapatkan dari serbuk terbaik adalah 7.88 %. Pada neraca massa, berat awal yang dimiliki bubur adalah sebesar 372.9 g. Setelah dilakukan proses pengeringan, berat akhir produk (serbuk) adalah sebesar 32.55 g sehingga rendemen serbuk ini adalah sebesar 8,73%. Pada saat proses pengeringan, awalnya laju pengeringan sebesar 16,72 kemudian turun berturut-turut sebesar 12,2365; 6,5485 hingga pada akhirnya sebesar 2,59. Total serbuk yang diperoleh dari 1 cabinet dryer adalah 976,5 g. Jika serbuk dimasukkan ke dalam kapsul dengan isi per kapsul ± 0,5 g, maka akan didapatkan kapsul sebanyak 1953. Apabila dalam sehari terdapat dua kali produksi, maka kapsul yang didapatkan sebanyak 2906 kapsul

Bayesian Optimization in Machine Learning

Bayesian optimization has risen over the last few years as a very attractive approach to find the optimum of noisy, expensive to evaluate, and possibly black-box functions. One of the fields where these functions are common is in machine-learning, where one typically has to fit a particular model by minimizing a specified form of loss. In this Master s thesis we first focus on reviewing the most recent literature on Gaussian Processes as well as Bayesian optimiza- tion methods, then we benchmark said methods against several real case machine-learning scenarios and lastly we provide open source software that will allow researchers to apply these strategies in other problems