Running Genetic Algorithms in the Edge: A First Analysis

Nowadays, the volume of data produced by different kinds of devices is continuously growing, making even more difficult to solve the many optimization problems that impact directly on our living quality. For instance, Cisco projected that by 2019 the volume of data will reach 507.5 zettabytes per year, and the cloud traffic will quadruple. This is not sustainable in the long term, so it is a need to move part of the intelligence from the cloud to a highly decentralized computing model. Considering this, we propose a ubiquitous intelligent system which is composed by different kinds of endpoint devices such as smartphones, tablets, routers, wearables, and any other CPU powered device. We want to use this to solve tasks useful for smart cities. In this paper, we analyze if these devices are suitable for this purpose and how we have to adapt the optimization algorithms to be efficient using heterogeneous hardware. To do this, we perform a set of experiments in which we measure the speed, memory usage, and battery consumption of these devices for a set of binary and combinatorial problems. Our conclusions reveal the strong and weak features of each device to run future algorihms in the border of the cyber-physical system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This research has been partially funded by the Spanish MINECO and FEDER projects TIN2014-57341-R (http://moveon.lcc.uma.es), TIN2016-81766-REDT (http://cirti.es), TIN2017-88213-R (http://6city.lcc.uma.es), the Ministry of Education of Spain (FPU16/02595

Cellular Genetic Algorithms: Understanding the Behavior of Using Neighborhoods

In this paper, we analyze the neighborhood effect in the selection of parents on an evolutionary algorithm. In this line, we compare a cellular genetic algorithm (cGA), which intrinsically uses the neighbor notion in the mating process, with a modified genetic algorithm including the concept of neighborhood in the selection of parents. Additionally, we analyze the neighborhood size considered for the selection of parent, trying to discover if a quasi-optimal size exists. All the analysis is carried out from a traditional analytic sense to a theoretical point of view regarding evolvability measures. The experimental results suggest that the neighbor effect is important in the performance of an evolutionary algorithm and could provide the cGA with higher chances of success in well-known optimization problems. Regarding the neighborhood size, there is an evidence that a range of neighbors of six, plus/minus two, individuals leads to the cGA to perform more efficiently than other considered sizes.Fil: Salto, Carolina. Universidad Nacional de La Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Confluencia; ArgentinaFil: Alba, Enrique. Universidad de Málaga; Españ

Controlador PID con algoritmos genéticos de números reales

A genetic algorithm (GA) is an artificial intelligence technique that can be applied to any engineering specialty. In this study, the proposed GA finds optimal values for PID controller parameters Kp, Ki and Kd, which are widely used in the industry. Chromosomes, composed of Kp, Ki and Kd genes, represented by real numbers, evolve and are evaluated by the mean square error (MSE) of the desired output. In that sense, the solution is the chromosome with the lowest MSE, which produces less transient output. In addition, the GA has been encoded in MATLAB language and results have been compared with other works.Un algoritmo genético o genetic algorithm (GA) es una técnica de la inteligencia artificial que es utilizada en cualquiera de las especialidades de ingeniería. En el presente estudio, el GA propuesto encuentra valores óptimos para los parámetros Kp, Ki y Kd de un controlador PID, utilizado ampliamente en la industria. Los cromosomas, formados con los genes Kp, Ki y Kd, representados por números reales, evolucionan y son evaluados mediante el error cuadrático medio (ECM) de la salida deseada. En ese sentido, la solución es el cromosoma con menor ECM, el cual produce menores transitorios en la salida. Además, el GA ha sido codificado en lenguaje M (MATLAB) y los resultados han sido comparados con otros trabajos

Refined Genetic Algorithms for Polypeptide Structure Prediction

Accurate and reliable prediction of macromolecular structures has eluded researchers for nearly 40 years. Prediction via energy minimization assumes the native conformation has the globally minimal energy potential. An exhaustive search is impossible since for molecules of normal size, the size of the search space exceeds the size of the universe. Domain knowledge sources, such as the Brookhaven PDB can be mined for constraints to limit the search space. Genetic algorithms (GAs) are stochastic, population based, search algorithms of polynomial (P) time complexity that can produce semi-optimal solutions for problems of nondeterministic polynomial (NP) time complexity such as PSP. Three refined GAs are presented: A farming model parallel hybrid GA (PHGA) preserves the effectiveness of the serial algorithm with substantial speed up. Portability across distributed and MPP platforms is accomplished with the Message Passing Interface (MPI) communications standard. A Real-valved GA system, real-valued Genetic Algorithm, Limited by constraints (REGAL), exploiting domain knowledge. Experiments with the pentapeptide Met-enkephalin have identified conformers with lower energies (CHARMM) than the accepted optimal conformer (Scheraga, et al), -31.98 vs -28.96 kcals/mol. Analysis of exogenous parameters yields additional insight into performance. A parallel version (Para-REGAL), an island model modified to allow different active constraints in the distributed subpopulations and novel concepts of Probability of Migration and Probability of Complete Migration

A Comparative Analysis of Darwinian Asexual and Sexual Reproduction in Evolutionary Robotics

Evolutionary Robotics systems draw inspiration from natural evolution to solve the problem of robot design. A key moment in the evolutionary process is reproduction, when the genotype of one or more parents is inherited by their offspring. Existent approaches have used both sexual and asexual reproduction but a comparison between the two is still missing. In this work, we study the effects of sexual and asexual reproduction on the controllers of an Evolutionary Robotics system. In our system, both morphologies and controllers are jointly evolved to solve two separate tasks. We adopt the Triangle of Life framework, in which the controllers go through a phase of learning before reproduction. Using extensive simulations we show that sexual reproduction of the robots' brains is preferable over asexual reproduction as it obtains better robots in terms of fitness. Moreover, we show that sexually reproducing robots present different morphologies and behaviors than the asexually reproducing ones, even though the reproduction mechanism only affects their brains. Finally, we study the effects of the reproduction mechanism on the robots' learning capabilities. By measuring the difference between the inherited and the learned brain we find that robots that evolved using sexual reproduction have better inherited brains and are also better learners

AN INVESTIGATION OF EVOLUTIONARY COMPUTING IN SYSTEMS IDENTIFICATION FOR PRELIMINARY DESIGN

This research investigates the integration of evolutionary techniques for symbolic regression. In particular the genetic programming paradigm is used together with other evolutionary computational techniques to develop novel approaches to the improvement of areas of simple preliminary design software using empirical data sets. It is shown that within this problem domain, conventional genetic programming suffers from several limitations, which are overcome by the introduction of an improved genetic programming strategy based on node complexity values, and utilising a steady state algorithm with subpopulations. A further extension to the new technique is introduced which incorporates a genetic algorithm to aid the search within continuous problem spaces, increasing the robustness of the new method. The work presented here represents an advance in the Geld of genetic programming for symbolic regression with significant improvements over the conventional genetic programming approach. Such improvement is illustrated by extensive experimentation utilising both simple test functions and real-world design examples