Douglas Mota Dias

Association of the anti-tuberculosis drug rifampicin with a PAMAM dendrimer

The association of the anti-tuberculosis drug rifampicin (RIF) with a 4th-generation poly(amidoamine) (G4-PAMAM) dendrimer was investigated by means of molecular dynamics simulations. The RIF load capacity was estimated to be around 20 RIF per G4-PAMAM at neutral pH. The complex formed by 20 RIF molecules and the dendrimer (RIF20-PAMAM) was subjected to 100 ns molecular dynamics (MD) simulations at two different pH conditions (neutral and acidic). The complex was found to be significantly more stable in the simulation at neutral pH compared to the simulation at low pH in which the RIF molecules were rapidly and almost simultaneously expelled to the solvent bulk. The high stability of the RIF-PAMAM complex under physiological pH and the rapid release of RIF molecules under acidic medium provide an interesting switch for drug targeting since the Mycobacterium resides within acidic domains of the macrophage. Altogether, these results suggest that, at least in terms of stability and pH-dependent release, PAMAM-like dendrimers may be considered suitable drug delivery systems for RIF and derivatives.

Evolving CUDA PTX programs by quantum inspired linear genetic programming

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.

GPF-CLASS: A Genetic Fuzzy model for classification

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.

Toward a Quantum-Inspired Linear Genetic Programming model

The huge performance superiority of quantum computers for some specific problems lies in their direct use of quantum mechanical phenomena (e.g. superposition of states) to perform computations. This has motivated the creation of quantum-inspired evolutionary algorithms (QIEAs), which successfully use some quantum physics principles to improve the performance of evolutionary algorithms (EAs) for classical computers. This paper proposes a novel QIEA (Quantum-Inspired Linear Genetic Programming - QILGP) for automatic synthesis of machine code (MC) programs and aims to present a preliminary evaluation of applying the quantum-inspiration paradigm to evolve programs by using two symbolic regression problems. QILGP performance is compared to AIMGP model, since it is the most successful genetic programming technique to evolve MC. In the first problem, the hit ratio of QILGP (100%) is greater than the one of AIMGP (77%). In the second problem, QILGP seems to carry on a less greedy search than AIMGP. Since QILGP presents some satisfactory results, this paper shows that the quantum-inspiration paradigm can be a competitive approach to evolve programs more efficiently, which encourages further developments of that first and simplest QILGP model with multiple individuals.

Refinery Scheduling Optimization using Genetic Algorithms and Cooperative Coevolution

Oil refineries are one of the most important examples of multiproduct continuous plants, that is, a continuous processing system that generates a number of products simultaneously. A refinery processes various crude oil types and produces a wide range of products. It is a complex optimization problem, mainly due to the number of different tasks involved and different objective criteria. In addition, some of the tasks have precedence constraints that require other tasks to be scheduled first. In this paper the refinery scheduling problem is addressed using genetic algorithms and cooperative coevolution. A simple refinery, with commonly found types of equipments, tasks and constraints of a real refinery, was created. Three test scenarios were designed with different sizes, demands and constraints. In all of them, the results obtained were far better than the ones obtained through random search

Automatic Synthesis of Microcontroller Assembly Code Through Linear Genetic Programming

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

Combining Forecasts: A Genetic Programming Approach

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.

Solving stochastic differential equations through genetic programming and automatic differentiation

Abstract This paper investigates the potential of evolutionary algorithms, developed using a combination of genetic programming and automatic differentiation, to obtain symbolic solutions to stochastic differential equations. Using the MATLAB programming environment and based on the theory of stochastic calculus, we develop algorithms and conceive a new methodology of resolution. Relative to other methods, this method has the advantages of producing solutions in symbolic form and in continuous time and, in the case in which an equation of interest is completely unknown, of offering the option of algorithms that perform the specification and estimation of the solution to the equation via a real database. The last advantage is important because it determines an appropriate solution to the problem and simultaneously eliminates the difficult task of arbitrarily defining the functional form of the stochastic differential equation that represents the dynamics of the phenomenon under analysis. The equation for geometric Brownian motion, which is usually applied to model prices and returns from financial assets, was employed to illustrate and test the quality of the algorithms that were developed. The results are promising and indicate that the proposed methodology can be a very effective alternative for resolving stochastic differential equations.

Quantum-Inspired Multi-gene Linear Genetic Programming Model for Regression Problems

We propose the Quantum-Inspired Multi-Gene Lin-ear Genetic Programming (QIMuLGP), which is a generalization of Quantum-Inspired Linear Genetic Programming (QILGP) model for symbolic regression. QIMuLGP allows us to explore a different genotypic representation (i.e. linear), and to use more than one genotype per individual, combining their outputs using least squares method (multi-gene approach). We used 11 benchmark problems to experimentally compare QIMuLGP with: canonical tree Genetic Programming, Multi-Gene tree-based GP (MGGP), and QILGP. QIMuLGP obtained better results than QILGP in almost all experiments performed. When compared to MGGP, QIMuLGP achieved equivalent errors for some experiments with its runtime always shorter (up to 20 times and 8 times on average), which is an important advantage in high dimensional-scalable problems.

Numerical optimization by multi-gene genetic programming

This paper presents a new method for numerical optimization problems based on Multi-Gene Genetic Programming. We discuss theoretical aspects, operators, representation, and experimental results.