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The performance of an evolutionary algorithm strongly depends on the design of its operators and on the management of these operators along the search; that is, on the ability of the algorithm to balance exploration and exploitation of the search space. Recent approaches automate the tuning and control of the parameters that govern this balance. We propose a new technique to dynamically control the behavior of operators in an EA and to manage a large set of potential operators. The best operators are rewarded by applying them more often. Tests of this technique on instances of 3-SAT return results that are competitive with an algorithm tailored to the problem.

Whether chemists or biologists, researchers dealing with metabolomics require tools to decipher complex mixtures. As a part of metabolomics and initially dedicated to identifying bioactive natural products, dereplication aims at reducing the usual timeconsuming process of known compounds isolation. Mass spectrometry and nuclear magnetic resonance are the most commonly reported analytical tools during dereplication analysis. Though it has low sensitivity, 13 C NMR has many advantages for such a study. Notably, it is nonspecific allowing simultaneous high-resolution analysis of any organic compounds including stereoisomers. Since NMR spectrometers nowadays provide useful data sets in a reasonable time frame, we have embarked upon writing software dedicated to 13 C NMR dereplication. The present study describes the development of a freely distributed algorithm, namely MixONat and its ability to help researchers decipher complex mixtures. Based on Python 3.5, MixONat analyses a { 1 H}-13 C NMR spectrum optionally combined with DEPT-135 and 90 datato distinguish carbon types (i.e., CH 3 , CH 2 , CH, and C)as well as a MW filtering. The software requires predicted or experimental carbon chemical shifts (δc) databases and displays results that can be refined based on user interactions. As a proof of concept, this 13

Abstract-The goal of Adaptive Operator Selection is the on-line control of the choice of variation operators within Evolutionary Algorithms. The control process is based on two main components, the credit assignment, that defines the reward that will be used to evaluate the quality of an operator after it has been applied, and the operator selection mechanism, that selects one operator based on some operators qualities. Two previously developed Adaptive Operator Selection methods are combined here: Compass evaluates the performance of operators by considering not only the fitness improvements from parent to offspring, but also the way they modify the diversity of the population, and their execution time; Dynamic Multi-Armed Bandit proposes a selection strategy based on the well-known UCB algorithm, achieving a compromise between exploitation and exploration, while nevertheless quickly adapting to changes. Tests with the proposed method, called ExCoDyMAB, are carried out using several hard instances of the Satisfiability problem (SAT). Results show the good synergetic effect of combining both approaches.

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.