Natural Optimization Research Articles

The Surprising Ease of Finding Optimal Solutions for Controlling Nonlinear Phenomena in Quantum and Classical Complex Systems

This Perspective addresses the often observed surprising ease of achieving optimal control of nonlinear phenomena in quantum and classical complex systems. The circumstances involved are wide-ranging, with scenarios including manipulation of atomic scale processes, maximization of chemical and material properties or synthesis yields, Nature's optimization of species' populations by natural selection, and directed evolution. Natural evolution will mainly be discussed in terms of laboratory experiments with microorganisms, and the field is also distinct from the other domains where a scientist specifies the goal(s) and oversees the control process. We use the word "control" in reference to all of the available variables, regardless of the circumstance. The empirical observations on the ease of achieving at least good, if not excellent, control in diverse domains of science raise the question of why this occurs despite the generally inherent complexity of the systems in each scenario. The key to addressing the question lies in examining the associated control landscape, which is defined as the optimization objective as a function of the control variables that can be as diverse as the phenomena under consideration. Controls may range from laser pulses, chemical reagents, chemical processing conditions, out to nucleic acids in the genome and more. This Perspective presents a conjecture, based on present findings, that the systematics of readily finding good outcomes from controlled phenomena may be unified through consideration of control landscapes with the same common set of three underlying assumptions─the existence of an optimal solution, the ability for local movement on the landscape, and the availability of sufficient control resources─whose validity needs assessment in each scenario. In practice, many cases permit using myopic gradient-like algorithms while other circumstances utilize algorithms having some elements of stochasticity or introduced noise, depending on whether the landscape is locally smooth or rough. The overarching observation is that only relatively short searches are required despite the common high dimensionality of the available controls in typical scenarios.

Wilavidin - a novel member of the avidin family that forms unique biotin-binding hexamers.

Nature's optimization of protein functions is a highly intricate evolutionary process. In addition to optimal tertiary folding, the intramolecular recognition among the monomers that generate higher-order quaternary arrangements is driven by stabilizing interactions that have a pivotal role for ideal activity. Homotetrameric avidin and streptavidin are regularly utilized in many applications, whereby their ultra-high affinity toward biotin is dependent on their quaternary arrangements. In recent years, a new subfamily of avidins was discovered that comprises homodimers rather than tetramers, in which the high affinity toward biotin is maintained. Intriguingly, several of the respective dimers have been shown to assemble into higher-order cylindrical hexamers or octamers that dissociate into dimers upon biotin binding. Here, we present wilavidin, a newly discovered member of the dimeric subfamily, forming hexamers in the apo form, which are uniquely maintained upon biotin binding with six high-affinity binding sites. Removal of the short C-terminal segment of wilavidin resulted in the presence of the dimer only, thus emphasizing the role of this segment in stabilizing the hexamer. Utilization of a hexavalent biotin-binding form of avidin would be beneficial for expanding the biotechnological toolbox. Additionally, this unique family of dimeric avidins and their propensity to oligomerize to hexamers or octamers can serve as a basis for protein oligomerization and intermonomeric recognition as well as cumulative interactions that determine molecular assemblies.

SOFIA: Selection of Medical Features by Induced Alterations in Numeric Labels

This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighting the features, (2) performing small perturbations on numeric labels and (3) selecting the features that are relevant in the trained multi-target prediction models. With the purpose of decreasing the computational cost in the SOFIA method, we consider those multi-objective optimization metaheuristics that support parallelization. In this sense, we propose an extension of the Natural Optimization (NO) approach for Simulated Annealing to support a multi-objective (MO) optimization. This proposed extension, called MONO, and some multiobjective evolutionary algorithms (MOEAs) are considered when performing the SOFIA method to improve prediction models in a multi-stage migraine treatment. This work also considers the adaptation of these metaheuristics to run on GPUs for accelerating the exploration of a larger space of solutions and improving results at the same time. The obtained results show that accuracies close to 88% are obtained with the MONO metaheuristic when employing eight threads and when running on a GPU. In addition, training times have been decreased from more than 8 h to less than 45 min when running the algorithms on a GPU. Besides, classification models trained with the SOFIA method only require 15 medical features or fewer to predict treatment responses. All in all, the methods proposed in this work remarkably improve the accuracy of multi-target prediction models for the OnabotulinumtoxinA (BoNT-A) treatment, while selecting those relevant features that allow us to know in advance the response to every stage of the treatment.

The upside-down water collection system of Syntrichia caninervis.

Desert plants possess highly evolved water conservation and transport systems, from the root structures that maximize absorption of scarce ground water(1-5), to the minimization of leaf surface area(6) to enhance water retention. Recent attention has focused on leaf structures that are adapted to collect water and promote nucleation from humid air(7-9). Syntrichia caninervis Mitt. (Pottiaceae) is one of the most abundant desert mosses in the world and thrives in an extreme environment with multiple but limited water resources (such as dew, fog, snow and rain), yet the mechanisms for water collection and transport have never been completely revealed. S. caninervis has a unique adaptation: it uses a tiny hair (awn) on the end of each leaf to collect water, in addition to that collected by the leaves themselves. Here we show that the unique multiscale structures of the hair are equipped to collect and transport water in four modes: nucleation of water droplets and films on the leaf hair from humid atmospheres; collection of fog droplets on leaf hairs; collection of splash water from raindrops; and transportation of the acquired water to the leaf itself. Fluid nucleation is accomplished in nanostructures, whereas fog droplets are gathered in areas where a high density of small barbs are present and then quickly transported to the leaf at the base of the hair. Our observations reveal nature's optimization of water collection by coupling relevant multiscale physical plant structures with multiscale sources of water.

Annealing placement by thermodynamic combinatorial optimization

Placement is key issue of integrated circuit physical design. There exist some techniques inspired in thermodynamics coping with this problem as Simulated Annealing. In this article, we present a combinatorial optimization method directly derived from both Thermodynamics and Information Theory. In TCO (Thermodynamic Combinatorial Optimization), two kinds of processes are considered: microstate and macrostate transformations. Applying the Shannon's definition of entropy to reversible microstate transformations, a probability of acceptance based on Fermi--Dirac statistics is derived. On the other hand, applying thermodynamic laws to macrostate transformations, an efficient annealing schedule is provided. TCO has been compared with a custom Simulated Annealing (SA) tool on a set of benchmark circuits for the FPGA (Field Programmable Gate Arrays) placement problem. TCO has provided the high-quality results of SA, while inheriting the adaptive properties of Natural Optimization (NO).

Evolution in the sky

Evolution by natural selection is Nature's optimization technique. Owing to genetic variation in populations, optimization proceeds in a parallel, rather than a serial, fashion. The advantages of this method have long been recognized in computer science and artificial intelligence, where genetic algorithms simulate the evolution of ‘populations’ of solutions to a problem, with both ‘sexual reproduction’ and ‘mutations’ leading to new solutions whose survival depends on their fitness. Now, genetic algorithms have been successfully applied to two aerospace problems. Géraud Granger and his colleagues at CENA, the Center for Aerial Navigation Studies in Toulouse, France, have applied genetic algorithms to air traffic control. Their program, simulated on actual air traffic data, finds optimal solutions that keep planes from coming too close to each other, whilst at the same time minimizing deviations from the planes’ original trajectories. And at Purdue University, William Crossley and Edwin Williams have applied a genetic algorithm to the complex problem of planning low satellite orbits in order for the group of satellites to cover as much territory as possible. In some cases, the solutions that they have evolved reduce gaps in coverage by 25% – which has surprised satellite engineers. MW