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The construction of a molecular topology file is a prerequisite for any classical molecular dynamics simulation. However, the generation of such a file may be very challenging at times, especially for large supramolecules. While many tools are available to provide topologies for large proteins and other biomolecules, the scientific community researching nonbiological systems is not equally well equipped. Here, we present a practical tool to generate topologies for arbitrary supramolecules: The pyPolyBuilder. In addition to linear polymer chains, it also provides the possibility to generate topologies of arbitrary, large, branched molecules, such as, e.g., dendrimers. Furthermore, it also generates reasonable starting structures for simulations of these molecules. pyPolyBuilder is a standalone command-line tool implemented in python. Therefore, it may be easily incorporated in persisting simulation pipelines on any operating systems and with different simulation engines. pyPolyBuilder is freely available on github: https://github.com/mssm-labmmol/pypolybuilder.

The advent of the Covid-19 pandemic has resulted in a global crisis making the health systems vulnerable, challenging the research community to find novel approaches to facilitate early detection of infections This open-up a window of opportunity to exploit machine learning and artificial intelligence techniques to address some of the issues related to this disease In this work, we address the classification of ten SARS-CoV-2 protein sequences related to Covid-19 using k-mer frequency as features and considering two objectives;classification performance and feature selection The first set of experiments considered the objectives one at the time, four techniques were used for the feature selection and twelve well known machine learning methods, where three are neural network based for the classification The second set of experiments considered a multiobjective approach where we tested a well known multi-objective approach Non-dominated Sorting Genetic Algorithm II (NSGA-II), and the Multi-dimensional Archive of Phenotypic Elites (MAP-Elites), which considers quality+diversity containers to guide the search through elite solutions The experimental results shows that ResNet and PCA is the best combination using single objectives Whereas, for the mulit-classification, NSGA-II outperforms ME with two out of three classifiers, while ME gets competitive results bringing more diverse set of solutions © 2021 by SCITEPRESS - Science and Technology Publications, Lda

Scalability problems have hindered the progress of Evolvable Hardware in tackling complex circuits. The two key issues are the amount of testing (for example, a 64-bit \(\times \) 64-bit add-shift multiplier problem has \(2^{64 + 64}\) test cases) and low level that hardware works at: a circuit to implement 64-bit \(\times \) 64-bit add-shift multiplier would require approximately 33,234 gates when synthesized using the powerful Yosys Open SYnthesis Suite tool. We use Grammatical Evolution and SystemVerilog, a Hardware Description Language (HDL), to evolve fully functional parameterized adder, multiplier and selective parity circuits with default input bit-width sizes of 64-bit + 64-bit, 64-bit \(\times \) 64-bit and 128-bit respectively.

Genetic programming techniques allow flexibility in the optimization process, making it possible to use them in different areas of knowledge and providing new ways for specialists to advance in their areas more quickly and more accurately. This work presents a preliminary analysis of a new method using multi-gene genetic programming to multimodal numerical optimization. The new algorithm uses some niching techniques based on the clear procedure to maintain the population diversity, in order to perform a multivariate mapping among initial guesses to optimal parameters for a system. We used a multi-modal benchmark set with different characteristics and difficulty levels to evaluate this new algorithm. Statistical analysis suggested that this new multi-modal method using multi-gene genetic programming can be used for problems that requires more than a single solution.

Econometric Genetic Programming (EGP) evolves multiple linear regressions through Genetic Programming (GP), which is responsible for model selection, aiming to generate high accuracy regressions with potential interpretability of parameters. It uses statistical significance as a feature selection tool, directly and efficiently identifying introns and controlling bloat. In this paper, EGP is tested against traditional feature-selection econometric algorithms in regression tasks - namely Partial Least Squares Regression, Ridge Regression and Stepwise Forward Regression - outperforming them in all three datasets. The way EGP explores search space of possible regressors and models is crucial for its results. EGP is carefully constructed considering econometric theory on cross-sectional datasets, giving rigorous treatment on topics like homoscedasticity and heteroscedasticity, statistical inference for estimated parameters and sampling criteria. It also benefits by the mathematical proof on accuracy and statistical significance: accuracy will only increase if the regressor presents a test's statistics module in a two-sided hypothesis testing higher than a predefined value.

Logistic Regression and Genetic Programming (GP) have already been compared to each other in classification tasks. In this paper, Econometric Genetic Programming (EGP), first introduced as a regression methodology, is extended to binary classification tasks and evolves logistic regressions through GP, aiming to generate high accuracy classifications with potential interpretability of parameters, while uses statistical significance as a feature-selection tool and GP for model selection. EGP-Classification (or EGP-C), the name of this proposed EGP’s extension, was tested against a large group of algorithms in three cross-sectional datasets, showing competitive results in most of them. EGP-C successfully competed against highly non-linear algorithms, like Support Vector Machines and Multilayer Perceptron with Back Propagation, and still allows interpretability of parameters and models generated.

Combining forecasts is a common practice in time series analysis. This technique involves weighing each estimate of different models in order to minimize the error between the resulting output and the target. This work presents a novel methodology, aiming to combine forecasts using genetic programming, a metaheuristic that searches for a nonlinear combination and selection of forecasters simultaneously. To present the method, the authors made three different tests comparing with the linear forecasting combination, evaluating both in terms of RMSE and MAPE. The statistical analysis shows that the genetic programming combination outperforms the linear combination in two of the three tests evaluated.

One of the most important issues in oil \& gas industry is the lithological identification. Lithology is the macroscopic description of the physical characteristics of a rock. This work proposes a new methodology for lithological discrimination, using GPF-CLASS model (Genetic Programming for Fuzzy Classification) a Genetic Fuzzy System based on Multi-Gene Genetic Programming. The main advantage of our approach is the possibility to identify, through seismic patterns, the rock types in new regions without requiring opening wells. Thus, we seek for a reliable model that provides two flexibilities for the experts: evaluate the membership degree of a seismic pattern to the several rock types and the chance to analyze at linguistic level the model output. Therefore, the final tool must afford knowledge discovery and support to the decision maker. Also, we evaluate other 7 classification models (from statistics and computational intelligence), using a database from a well located in Brazilian coast. The results demonstrate the potentialities of GPF-CLASS model when comparing to other classifiers.

This work presents a Genetic Fuzzy Classification System (GFCS) called Genetic Programming Fuzzy Classification System (GPF-CLASS). This model differs from the traditional approach of GFCS, which uses the metaheuristic as a way to learn “if-then” fuzzy rules. This classical approach needs several changes and constraints on the use of genetic operators, evaluation and selection, which depends primarily on the metaheuristic used. Genetic Programming makes this implementation costly and explores few of its characteristics and potentialities. The GPF-CLASS model seeks for a greater integration with the metaheuristic: Multi-Gene Genetic Programming (MGGP), exploring its potential of terminals selection (input features) and functional form and at the same time aims to provide the user with a comprehension of the classification solution. Tests with 22 benchmarks datasets for classification have been performed and, as well as statistical analysis and comparisons with others Genetic Fuzzy Systems proposed in the literature.

The tremendous computing power of Graphics Processing Units (GPUs) can be used to accelerate the evolution process in Genetic Programming (GP). The automatic generation of code using the GPU usually follows two different approaches: compiling each evolved or interpreting multiple programs. Both approaches, however, have performance drawbacks. In this work, we propose a novel approach where the GPU pseudo-assembly language, PTX (Parallel Thread Execution), is evolved. Evolving PTX programs is faster, since the compilation of a PTX program takes orders of magnitude less time than a CUDA program compilation on the CPU, and no interpreter is necessary. Another important aspect of our approach is that the evolution of PTX programs follows the Quantum Inspired Linear Genetic Programming (QILGP). Our approach, called QILGP3U (QILGP + GPGPU), enables the evolution on a single machine in a reasonable time, enhances the quality of the model with the use of PTX, and for big databases can be much faster than the CPU implementation.

This chapter considers the application of linear genetic programming in the automatic synthesis of microcontroller assembly language programs that implement strategies for time-optimal or sub-optimal control of the system to be controlled, based on mathematical modeling through dynamic equations. One of the difficulties presented by the conventional design of optimal control systems lies in the fact that solutions to problems of this type normally involve a highly non-linear function of the system’s state variables. As a result, it is often not possible to find an exact mathematical solution. As for the implementation of the controller, there arises the difficulty of programming the microcontroller manually in order to execute the desired control. The research that has been done in the area of automatic synthesis of assembly language programs for microcontrollers through genetic programming is surveyed in this chapter and a novel methodology in which assembly language programs are automatically synthesized, based on mathematical modeling through dynamic plant equations, is introduced. The methodology is evaluated in two case studies: the cart-centering problem and the inverted pendulum problem. The control performance of the synthesized programs is compared with that of the systems obtained by means of a tree-based genetic programming method. The synthesized programs proved to perform at least as well, but they had D. M. Dias et al.: Automatic Synthesis of Microcontroller Assembly Code Through Linear

Typically, control system design leads to a higher-order non-linear function of the system's state variables. As a result, it is very hard to find a satisfactory mathematical solution. On the other hand, considering a microcontroller based implementation, another difficulty is to program it to carry out the desired control algorithm. This paper presents the application of linear genetic programming in the automatic synthesis of a microcontroller assembly program, which performs an optimized control of a water bath plant. The synthesis starts from the plant's mathematical modeling and supplies directly a assembly code for the microcontroller platform. When comparing the control performance of the synthesized program with that of a neuro-fuzzy based controller, the synthesized program proved to perform slightingly better.