Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain independent and multilingual by processing text using only language-independent techniques. EvoMSA is a classifier, based on Genetic Programming, that works by combining the output of different text classifiers and text models to produce the final prediction. We analyze EvoMSA, with its parameters fixed, on different SA competitions to provide a global overview of its performance, and as the results show, EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSA's components to measure their contribution to the performance; the idea is to facilitate a practitioner or newcomer to implement a competitive SA classifier. Finally, it is worth to mention that EvoMSA is available as open source software.

Inferring universal laws of the environment is an important ability of human intelligence as well as a symbol of general AI. In this paper, we take a step toward this goal such that we introduce a new challenging problem of inferring invariant physical equation from visual scenarios. For instance, teaching a machine to automatically derive the gravitational acceleration formula by watching a free-falling object. To tackle this challenge, we present a novel pipeline comprised of an Observer Engine and a Physicist Engine by respectively imitating the actions of an observer and a physicist in the real world. Generally, the Observer Engine watches the visual scenarios and then extracting the physical properties of objects. The Physicist Engine analyses these data and then summarizing the inherent laws of object dynamics. Specifically, the learned laws are expressed by mathematical equations such that they are more interpretable than the results given by common probabilistic models. Experiments on synthetic videos have shown that our pipeline is able to discover physical equations on various physical worlds with different visual appearances.

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.

The typical pipeline for a supervised machine learning project involves firstly the collection of a significant sample of labelled examples typically referred to as training data. Depending on whether the labels are continuous or categorical, the supervised learning task is known as regression or classification respectively. Next, once the training data is sufficiently clean and complete, it is used to directly build a predictive model using the machine learning algorithm of choice. The predictive model is then used to label new unlabelled examples, and if the labels of the new examples are known a priori by the user (but not used by the learning algorithm) then the predictive accuracy of the model can be evaluated. Different models can therefore be directly compared. In the usual case, the training data is "real", i.e. the model is learned directly from labelled examples that were collected specifically for that purpose. However, quite frequently, modifications are made to the training data after it is collected. For example, it is standard practice to remove outlier examples and normalise numeric values. Moreover, the machine learning algorithm itself may specify modifications to the training data.

The algorithm follows a protocol that the researchers refer to as biomimetic and which encodes quantum behaviours adapted to the same behaviours of living systems. Quantum biomimetics involves reproducing in quantum systems certain properties exclusive to living beings, and this research group had previously managed to imitate life, natural selection, learning and memory by means of quantum systems. This research aimed, as the authors themselves describe, "to design a set of quantum algorithms based on the imitation of biological processes, which take place in complex organisms, and transfer them to a quantum scale, so we were only trying to imitate the key aspects in these processes." In the scenario of artificial life that they designed, a set of models of simple organisms are capable of accomplishing the most common phases of life in a controlled virtual environment, and have proven that microscopic quantum systems are able to encode quantum characteristics and biological behaviours that are normally associated with living systems and natural selection. The models of organism designed were coined as units of quantum life, each one of which is made up of two qubits that act as genotype and phenotype, respectively, and where the genotype contains the information that describes the type of living unit, and this information is transmitted from generation to generation.

Inverse design is an outstanding challenge in disordered systems with multiple length scales such as polymers, particularly when designing polymers with desired phase behavior. We demonstrate high-accuracy tuning of poly(2-oxazoline) cloud point via machine learning. With a design space of four repeating units and a range of molecular masses, we achieve an accuracy of 4 {\deg}C root mean squared error (RMSE) in a temperature range of 24-90 {\deg}C, employing gradient boosting with decision trees. The RMSE is >3x better than linear and polynomial regression. We perform inverse design via particle-swarm optimization, predicting and synthesizing 17 polymers with constrained design at 4 target cloud points from 37 to 80 {\deg}C. Our approach challenges the status quo in polymer design with a machine learning algorithm, that is capable of fast and systematic discovery of new polymers.

Abstract--As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graphbased architectureis employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design. I. INTRODUCTION Various models have been thoroughly investigated by the machine learning (ML) community. In theory, these models are general and applicable to both academia and industry. However, it could be time-consuming to build a solution on a specific ML task, even for a ML expert.

Learning classifier systems are adaptive learning systems which have been widely applied in a multitude of application domains. However, there are still some generalization problems unsolved. The hurdle is that fitness and niching pressures are difficult to balance. Here, a new algorithm called Self Organizing Classifiers is proposed which faces this problem from a different perspective. Instead of balancing the pressures, both pressures are separated and no balance is necessary. In fact, the proposed algorithm possesses a dynamical population structure that self-organizes itself to better project the input space into a map. The niched fitness concept is defined along with its dynamical population structure, both are indispensable for the understanding of the proposed method. Promising results are shown on two continuous multi-step problems. One of which is yet more challenging than previous problems of this class in the literature.

Noname manuscript No. (will be inserted by the editor) Abstract Learning classifier systems are evolutionary machine learning algorithms, flexible enough to be applied toreinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifierswere proposed which are similar to learning classifier systems but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However, more tests and analysis are required to verify its benefits. Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM). This algorithm isapplied in challenging problems such as big, noisy as well as dynamically changing continuous inputaction mazes(growing and compressing mazes are included) withgood performance. Moreover, a genetic operator is proposed which utilizes the topological information ofthe SOM's population structure, improving the results. Thus, the first steps in structured evolutionary machinelearning are shown, nonetheless, the problems faced are more difficult than the state-of-art continuous input-action multi-step ones. 1 Introduction Learning Classifier Systems (LCS) are several algorithms inspired by evolution [29],[20]. Different from most reinforcement learning algorithms, however, LCS algorithms do not use state-action lookup tables to predict payoff. In this manner, the difficulties that arrive from complex problems, wherea large number of states and/or actions are required, can be avoided. Oneway of solving this problem is to separate a fitness defined on a niche from fitnesses defined on other niches (i.e., having a good fitness on other niches would not influence the present niche).

The ability to sequence genomes quickly has provided scientists with reams of data, but understanding how evolution has shaped humans is still a difficult task.Credit: Guy Tear/Wellcome Coll./CC Pinpointing where and how the human genome is evolving can be like hunting for a needle in a haystack. Each person's genome contains three billion building blocks called nucleotides, and researchers must compile data from thousands of people to discover patterns that signal how genes have been shaped by evolutionary pressures. To find these patterns, a growing number of geneticists are turning to a form of machine learning called deep learning. Proponents of the approach say that deep-learning algorithms incorporate fewer explicit assumptions about what the genetic signatures of natural selection should look like than do conventional statistical methods.