Analysis of the Clearing Diversity-Preserving Mechanism

Clearing is a niching method inspired by the principle of assigning the available resources among a subpopulation to a single individual. The clearing procedure supplies these resources only to the best individual of each subpopulation: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity mechanism. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and runtime analysis to explain how and why it is a powerful method. We prove that a (mu+1) EA with large enough population size and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that a (mu+1) EA with phenotypic and genotypic distances is able to find both optima in TWOMAX for large niches in polynomial expected time

Quantum Moment Map and Invariant Integration Theory on Quantum Spaces

It is shown that, on the one hand, quantum moment maps give rise to examples for the operator-theoretic approach to invariant integration theory developed by K.-D. Kürsten and the second author, and that, on the other hand, the operator-theoretic approach to invariant integration theory is more general since it also applies to examples without a well-defined quantum moment map

Runtime analysis of crowding mechanisms for multimodal optimisation

Many real-world optimisation problems lead to multimodal domains and require the identification of multiple optima. Crowding methods have been developed to maintain population diversity, to investigate many peaks in parallel and to reduce genetic drift. We present the first rigorous runtime analyses of probabilistic crowding and generalised crowding, embedded in a (mu+1)EA. In probabilistic crowding the offspring compete with their parent in a fitness-proportional selection. Generalised crowding decreases the fitness of the inferior solution by a scaling factor during selection. We consider the bimodal function TwoMax and introduce a novel and natural notion for functions with bounded gradients. For a broad range of such functions we prove that probabilistic crowding needs exponential time with overwhelming probability to find solutions significantly closer to any global optimum than those found by random search. Even when the fitness function is scaled exponentially, probabilistic crowding still fails badly. Only if the exponential's base is linear in the problem size, probabilistic crowding becomes efficient on TwoMax. A similar threshold behaviour holds for generalised crowding on TwoMax with respect to the scaling factor. Our theoretical results are accompanied by experiments for TwoMax showing that the threshold behaviours also apply to the best fitness found

Speeding Up Evolutionary Multi-objective Optimisation Through Diversity-Based Parent Selection

Parent selection in evolutionary algorithms for multi-objective optimization is usually performed by dominance mechanisms or indicator functions that prefer non-dominated points, while the reproduction phase involves the application of diversity mechanisms or other methods to achieve a good spread of the population along the Pareto front. We propose to refine the parent selection on evolutionary multi-objective optimization with diversity-based metrics. The aim is to focus on individuals with a high diversity contribution located in poorly explored areas of the search space, so the chances of creating new non-dominated individuals are better than in highly populated areas. We show by means of rigorous runtime analysis that the use of diversity-based parent selection mechanisms in the Simple Evolutionary Multi-objective Optimiser (SEMO) and Global SEMO for the well known bi-objective functions OneMinMax and Lotz can significantly improve their performance. Our theoretical results are accompanied by additional experiments that show a correspondence between theory and empirical results

On the utilization of pair-potential energy functions in multi-objective optimization

In evolutionary multi-objective optimization (EMO), the pair-potential energy functions (PPFs) have been used to construct diversity-preserving mechanisms to improve Pareto front approximations. Despite PPFs have shown promising results when dealing with different Pareto front geometries, there are still some open research questions to improve the way we employ them. In this paper, we answer three important questions: (1) what is the effect of a crucial parameter of some PPFs?, (2) how do we set the optimal parameter value?, and (3) what is the best PPF in EMO? To solve these questions, we designed a brand-new fast algorithm to generate an approximate solution to a PPF-based subset selection problem and, then, we conducted a comprehensive parametrical study to predict the optimal parameter values using a deep neural network. To show the effectiveness of the PPF-based diversity-preserving mechanisms, we selected two application cases: the generation of reference point sets of benchmark problems (DTLZ, WFG, IDTLZ, IWFG, IMOP, and Viennet) with different Pareto front shapes, and the definition of a PPF-based archive that can be coupled to any multi-objective evolutionary algorithm to construct well-diversified Pareto front approximations. Using several diversity indicators, it is shown that the utilization of PPF-based mechanisms lead to good Pareto front approximations regardless of the Pareto front shape

The spider community in organic cotton crops in southern Spain

Frente al manejo tradicional, el cultivo ecológico proporciona una alternativa a los problemas de aparición de resistencias, residuos y contaminación ambiental, además de favorecer un aumento de la biodiversidad del ecosistema por el efecto beneficioso sobre la fauna auxiliar. En los agroecosistemas, la acción conjunta de los enemigos naturales de los fitófagos puede mantener a las poblaciones de muchos de ellos por debajo del umbral económico de daño. Uno de los grupos de depredadores menos conocidos en los estudios de control natural de las plagas del algodón son las arañas (Orden Araneae), de modo que se realizaron prospecciones en parcelas de algodón orgánico situadas en el sur de España: Córdoba, Sevilla y Cádiz. Los ejemplares se recolectaron mediante la batida directa de las plantas. La especie más abundante resultó ser Cheiracanthium sp. (Miturgidae), seguida de otras especies como Philodromus sp. (Philodromidae), los tomísidos Thomisus onustus, Runcinia grammica y Synema globosum (Thomisidae), Salticus sp. (Salticidae) y Larinia lineata (Araneidae). Además se recogieron individuos de las familias Theridiidae y Linyphiidae. Este trabajo, por tanto, es una primera aproximación en el conocimiento de las especies y su abundancia en el cultivo ecológico del algodón, lo que supone una base para futuras investigaciones en el marco del control de fitófagos mediante arañas.Compared to traditional management, organic farming provides an alternative to the problems of the appearance of resistance, residues and environmental pollution, as well as favoring an increase in ecosystem biodiversity through beneficial effects on other fauna. In agroecosystems, the combined action of natural enemies on phytophages can maintain populations of many of them below economic damage thresholds. One of the least known groups of predators in studies on the natural control of cotton pests are the spiders (Order Araneae), therefore surveys were conducted in plots of organic cotton situated in southern Spain: Córdoba, Sevilla and Cádiz. Specimens were collected by beating the plants. The most abundant species was Cheiracanthium sp. (Miturgidae), followed by other species such as Philodromus sp. (Philodromidae), the crab spiders Thomisus onustus, Runcinia grammica and Synema globosum (Thomisidae), Salticus sp. (Salticidae) and Larinia lineata (Araneidae). In addition, individuals were collected from the families Theridiidae and Linyphiidae. This work, therefore, is a first step in understanding the species and their abundance in organic cotton crops, and will be the basis for future research in the area of phytophage control by spider

Geometrical design for pure current-driven domain wall nucleation and shifting

[EN]Nucleation of domain walls by current-driving a single domain wall, confined to the junction area of two symmetrical strips, is investigated using systematic micromagnetic simulations. Secondary domain walls (equivalently, bits encoded in domains) are simultaneously nucleated and driven by alternatively applying current pulses between two terminals in the structure. Simulations show that nanosecond-duration current pulses nucleate and drive series of robust up/down domains even under realistic conditions. These results demonstrate a technique for sequentially nucleating and shifting domain walls without using attached external “bit lines,” fields, or modifying the ferromagnetic strip

Current-Driven Domain Wall Motion in Curved Ferrimagnetic Strips Above and Below the Angular Momentum Compensation

[EN] Current driven domain wall motion in curved Heavy Metal/Ferrimagnetic/Oxide multilayer strips is investigated using systematic micromagnetic simulations which account for spinorbit coupling phenomena. Domain wall velocity and characteristic relaxation times are studied as functions of the geometry, curvature and width of the strip, at and out of the angular momentum compensation. Results show that domain walls can propagate faster and without a significant distortion in such strips in contrast to their ferromagnetic counterparts. Using an artificial system based on a straight strip with an equivalent current density distribution, we can discern its influence on the wall terminal velocity, as part of a more general geometrical influence due to the curved shape. Curved and narrow ferrimagnetic strips are promising candidates for designing high speed and fast response spintronic circuitry based on current-driven domain wall motion.Projects SA114P20 and SA299P18 from Junta de Castilla y Leon (JCyL) MAT2017-87072-C4-1-P and PID2020-117024GB-C41 from the Ministry of Economy, Spanish government MAGNEFI, from the European Commission (European Union

Knowledge management in small companies in Tepic, México.

La administración del conocimiento ha tenido un considerable avance en años recientes, lo cual ha incidido en la aparición de estudios sobre cómo una organización puede adquirir, almacenar, desarrollar y compartir el conocimiento generado por sus miembros. Este estudio explora y analiza 40 pequeñas empresas de menudeo en Nayarit, mediante la metodología propuesta por el eKnowledge Center (2000), cuya encuesta se utiliza como modelo de administración del conocimiento en las empresas estudiadas