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The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a new effective matrix adaptation evolution strategy (MADFO) for noisy derivative-free optimization problems. Like every MAES solver, MADFO consists of three phases: mutation, selection and recombination. MADFO improves the mutation phase by generating good step sizes, neither too small not too large, that increase the probability of selecting mutation points with small inexact function values in the selection phase. In the recombination phase, a recombination point with lowest inexact function value found among all evaluated points so far may be found by a new randomized non-monotone line search method and accepted as the best point. If no best point is found, a heuristic point may be accepted as the best point. We compare MADFO with state-of-the-art DFO solvers on noisy test problems obtained by adding various kinds and levels of noise to all unconstrained CUTEst test problems with dimensions n ≤ 20 , and find that MADFO has the highest number of solved problems [ABSTRACT FROM AUTHOR]

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This paper deals with an N P -hard problem called the colored balanced traveling salesman problem (CBTSP), which is a variation of colored traveling salesman problem (CTSP) which in turn is a variation of multiple traveling salesman problem. To effectively solve this problem, an approach based on evolution strategy is proposed where mutation operator is designed taking into consideration the characteristics of CBTSP. The results obtained through our approach have been compared with the results of the novel genetic algorithm (NGA) which is the best approach available in the literature. The results show that our approach surpasses the results reported in the literature for majority of the instances in shorter amount of time. [ABSTRACT FROM AUTHOR]

The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup.

An evolution strategy-type heuristic simulation tool was developed to model the inverse problem of partial melting. The complexity of such a problem is expressed by the non-uniqueness of solution and the high number of variables it could have: 1 denoting the degree of freedom + m from the number of minerals of the rock + q from the number of rare earth elements. This motivated us to redefine the problem as an optimization one deriving an objective function from the batch equation to model the source composition and the partial melting degree from the chemistry of near-primary liquids. Then, we optimize the inverse modeling by using a heuristic approach, namely, Evolution Strategy. In the search algorithm structure it was considered: (a) the geochemical system constraints, (b) an initialization step, and (c) a procedure of mutation and heuristic individual selection. The heuristic simulation was successfully applied in four study cases, as mineralogical and rare earth element (REE) composition of known peridotitic sources. The partial melting conditions were reproduced with a deviation ≤ 10 - 6 in a reasonably practical time ( ∼ 2 hours), in the search for 1000 solutions, by using a desktop computer. Moreover, our technique provides several efficient solutions, due to non-uniqueness, which allows to explore different scenarios when when a priori knowledge is missing. On the other hand, the algorithm is also able to approach the original melt when knowing only n + 1 variables of it. The flexibility in the construction of our complete methodology, joined to the successfully results and the possibility to apply it to any trace element, suggests its extension to similar magmatic processes. [ABSTRACT FROM AUTHOR]

Multi-agent cooperation and confrontation technology have achieved rapid development in recent years. Most extant multi-agent reinforcement learning algorithms simplify the problem by using shared weights or local observation, and are only suitable for scenarios with less than ten agents. Given this, large-scale scene research needs to explore new directions. This paper presents a large-scale multi-agent evolutionary reinforcement jointed method. The multi-agent learning task is separated into numerous stages based on the agent's scale, and the self-attention mechanism is utilized to handle changing numbers of agents in each step. Simultaneously, to avoid the agents' poor adaptability in previous stages, the best individuals in the population are chosen at each stage of training via evolutionary techniques. Two typical unmanned aerial vehicle cluster missions, multi-domain joint sea crossing and landing missions, were created to validate the performance of the suggested technique, and the operational rules and reward functions were also given. Experiments have shown that the model trained using the suggested method has good performance and stability and can provide a multi-agent collaborative decision-making model suitable for large-scale environments. [ABSTRACT FROM AUTHOR]

Although t-way strategy tries to generate a minimum test suite (TS) for detecting errors in software systems, its functionality is affected by three important challenges. The first one, which relates to the quality of the generated TS, expresses that some complex errors (e.g., deadlocks in concurrent systems) may not be detected through the generated TS. The second one is that manually preparing parameters and their values in the modern software systems is difficult or even impossible, whereas the third one is the low generation speed and the large size of the generated test suite. In this paper, we propose a three-phase approach (so-called TPA) to handle these challenges. It seems that injecting some information about special errors into the test suite can raise its quality. For this purpose, TPA, in the first phase, uses an optimized version of model checking to extract such information from a model of the system under test. The extracted information is then injected into the test suite. In the second phase, TPA uses the generated state space in the first phase to automatically prepare parameters and their values. In the last phase, TPA applies an adopted version of evolution strategy to improve the functionality of t-way strategy in terms of generation speed and test suite size. Multiple and pairwise comparisons of results confirm that TPA has the best functionality in comparison with other evolutionary algorithms. [ABSTRACT FROM AUTHOR]

Automating the process of tuning the methods for solving systems of linear algebraic equations is an actual issue that can simplify the process of performing the calculations and improve their performance. This paper discusses ways of improving the hybrid evolution strategy proposed earlier by the authors to deal with this problem. Several aspects of evolution strategy mutation operators, including the locking of soft mutations for some parameters, optimizing the continuous parameters, adjusting the parameter search space, and tuning the parameter change stepping, are evaluated. The efficiency of the proposed modifications is investigated in detail, and the potential performance improvements in terms of the linear system optimal solution times and the optimization algorithm calculation times are outlined. [ABSTRACT FROM AUTHOR]

Bayesian method is commonly used in the reliability assessment of repairable systems with small samples in fields such as aerospace.The knowledge about the informative prior and/or failure data from repairable systems may not be collected as precisely as possible due to uncertain factors,but can be obtained in terms of lower and upper bounds.For the case of such interval uncertainty information,the Bayesian reliability analysis method of multiple repairable whose failure process can follow the power law process(PLP)is studied in this paper.Under the informative priors,the interval-based Bayesian posterior analysis of the PLP is essentially transformed into solving the constrained optimization problem in which the objective function is just the traditional Bayesian posterior analysis result of the PLP under the informative priors.The feasibility and effectiveness of the proposed method is verified with specific enginee-ring examples.The results show that the proposed interval-based Bayesian method of the PLP can provide a refe-rence method for the reliability assessment of repairable systems with small samples in consideration of the influence of uncertain factors.

Abstract Background When designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur throughout facial growth because the rate and direction of growth can greatly affect the necessity of using different treatment mechanics. This paper presents a Bayesian network approach for facial biotype classification to classify patients’ biotypes into Dolichofacial (long and narrow face), Brachyfacial (short and wide face), and an intermediate kind called Mesofacial, we develop a novel learning technique for tree augmented Naive Bayes (TAN) for this purpose. Results The proposed method, on average, outperformed all the other models based on accuracy, precision, recall, $$F_{1}\hbox {-score}$$ F 1 -score , and kappa, for the particular dataset analyzed. Moreover, the proposed method presented the lowest dispersion, making this model more stable and robust against different runs. Conclusions The proposed method obtained high accuracy values compared to other competitive classifiers. When analyzing a resulting Bayesian network, many of the interactions shown in the network had an orthodontic interpretation. For orthodontists, the Bayesian network classifier can be a helpful decision-making tool.

Learning emotion assessment is a non-negligible step in analyzing learners' cognitive processing. Data are the basis of the learning emotion assessment. However, the existing learning emotion assessment models cannot balance model accuracy and interpretability well due to the influence of uncertainty in the process of data collection and model parameter errors. Given the above problems, a new learning emotion assessment model based on evidence reasoning and a belief rule base (E-BRB) is proposed in this paper. First, the transformation matrix is introduced to transform multiple emotional indicators into the same standard framework and integrate them, which keeps the consistency of information transformation. Second, the relationship between emotional indicators and learning emotion states is modeled by E-BRB in conjunction with expert knowledge. In addition, we employ the projection covariance matrix adaptation evolution strategy (P-CMA-ES) to optimize the model parameters and improve the model's accuracy. Finally, to demonstrate the effectiveness of the proposed model, it is applied to emotion assessment in science learning. The experimental results show that the model has better accuracy than data-driven models such as neural networks. [ABSTRACT FROM AUTHOR]

In a distribution system, sparse reliable samples and inconsistent fault characteristics always appear in the dataset of neural network fault detection models because of high impedance fault (HIF) and system structural changes. In this paper, we present an algorithm called Generative Adversarial Networks (GAN) based on the Reptile Algorithm (GANRA) for generating fault data and propose an evolution strategy based on GANRA to assist the fault detection of neural networks. First, the GANRA generates enough high-quality analogous fault data to solve a shortage of realistic fault data for the fault detection model’s training. Second, an evolution strategy is proposed to help the GANRA improve the fault detection neural network’s accuracy and generalization by searching for GAN’s initial parameters. Finally, Convolutional Neural Network (CNN) is considered as the identification fault model in simulation experiments to verify the validity of the evolution strategy and the GANRA under the HIF environment. The results show that the GANRA can optimize the initial parameters of GAN and effectively reduce the calculation time, the sample size, and the number of learning iterations needed for dataset generation in the new grid structures.

Training a robot that engages with people is challenging; it is expensive to directly involve people in the training process, which requires numerous data samples. This paper presents an alternative approach for resolving this problem. We propose a human path prediction network (HPPN) that generates a user’s future trajectory based on sequential robot actions and human responses using a recurrent-neural-network structure. Subsequently, an evolution-strategy-based robot training method using only the virtual human movements generated using the HPPN is presented. It is demonstrated that our proposed method permits sample-efficient training of a robotic guide for visually impaired people. By collecting only 1.5 K episodes from real users, we were able to train the HPPN and generate more than 100 K virtual episodes required for training the robot. The trained robot precisely guided blindfolded participants along a target path. Furthermore, using virtual episodes, we investigated a new reward design that prioritizes human comfort during the robot’s guidance without incurring additional costs. This sample-efficient training method is expected to be widely applicable to future robots that interact physically with humans.

By modeling the tumor sensitization and targeting (TST) as a natural computational process, we have proposed the framework of nanorobots-assisted in vivo computation. The externally manipulable nanorobots are steered to detect the tumor in the high-risk tissue, which is analogous to the process of searching for the optimal solution by the computing agents in the search space. To overcome the constraint of a nanorobotic platform that can only generate a uniform magnetic field to actuate the nanorobots, we have proposed the weak priority evolution strategy (WP-ES) in our previous works. However, these works do not consider the proportions of the nanorobot control and tracking operations, which are part and parcel of in vivo computation as the control operation aims at searching for the tumor effectively while the tracking mode is used for gathering information about the biological gradient function (BGF). Careful planning about the durations spent in these operations is needed for optimal performance of the TST strategy. To account for this issue, in the current article, we propose a novel computational principle, called the tension-relaxation (T-R) principle, to balance the displacements of nanorobots during each control and tracking cycle. Furthermore, we build three tumor vascular models with different sizes to represent three different targeting regions as the morphology of tumor vasculature determined by the tumor growth process is an important factor affecting TST. We carry out the computational experiments for tumors with three different sizes for several representative landscapes by introducing the T-R principle into the WP-ES-based swarm intelligence algorithms and considering the realistic internal constraints. The experimental outcomes demonstrate the effectiveness of the proposed TST strategy. [ABSTRACT FROM AUTHOR]

Multicolor holography, which can store and reconstruct wavefront information of optical waves at multiple wavelength channels, is demonstrated as a powerful platform for colorful image display. Recently, interleaved and segmented metasurfaces have emerged as appealing alternatives to realize the multicolor holography. However, the crosstalk among different wavelength channels can severely lower their performance. How to obtain the nanostructures with on‐demand resonance wavelength, bandwidth, and phase delay is the key to overcome this challenge. Here, a hybrid framework composed of a neural network and an evolutionary strategy is proposed to implement the inverse design of nanostructures with desired resonance wavelength, bandwidth, and phase delay. With the proposed hybrid framework, the crosstalk between different wavelength channels can be eliminated by precisely controlling the resonance wavelength and the bandwidth of every nanostructure. As a proof of concept, a multicolor meta‐holography for linear polarized light is experimentally and theoretically validated. The proposed hybrid framework provides a powerful platform for the design of metasurfaces for multi‐frequency optical manipulation and multiplexing. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is a complex manufacturing process where the correlation of the individual process parameters is not fully known. When introducing new materials or complex, individual geometries in EDM, a satisfactory vector of parameters for the process must be found. This challenge is often encountered in the aerospace industry as well as in mold and tool making. One previously successful method to tackle this challenge is the stochastic optimization procedure Evolutionary Strategy (ES). Utilizing an appropriately chosen objective function, the search for a suitable vector of input parameters may be formulated as a mathematical minimization problem over the parameter space. The ES with a covariance matrix adaption (CMA) was utilized to sample from this parameter space in a stochastic manner. Examining the impact of the CMA within an ES is a promising way to gain better insight into the performance of ES. For this purpose, a vector of input parameters was optimized for a drilling EDM process with a comma and a nested comma ES with CMA. It was found that the comma ES led to a reduction in erosion duration tero of 38 % compared to the initially chosen parameters and the nested comma ES led to a reduction in erosion duration tero of 27 % compared to the same initial parameters. The additional information stored in the covariance matrix enables the ES to find a local optimum of the parameter vector faster and more consistently. This fact is verified by use of visualizations of the covariance matrix on a two-dimensional subspace. From these findings it can be concluded, that for the application of the ES to the optimization of EDM processes the CMA should be performed continuously over all generations as opposed to resetting this matrix after a number of generations. [ABSTRACT FROM AUTHOR]

Several evolution strategies for in vivo computation are proposed with the aim of realizing tumor sensitization and targeting (TST) by externally manipulable nanoswimmers. In such targeting systems, nanoswimmers assembled by magnetic nanoparticles are externally manipulated to search for the tumor in the high-risk tissue by a rotating magnetic field produced by a coil system. This process can be interpreted as in vivo computation, where the tumor in the high-risk tissue corresponds to the global maximum or minimum of the in vivo optimization problem, the nanoswimmers are seen as the computational agents, the tumor-triggered biological gradient field (BGF) is used for fitness evaluation of the agents, and the high-risk tissue is the search space. Considering that the state-of-the-art magnetic nanoswimmer control method can only actuate all the nanoswimmers heading in the same direction simultaneously, we introduce the orthokinetic movement strategies into the agent location updating in the existing swarm intelligence algorithms. Especially, the gravitational search algorithm (GSA) is revisited and the corresponding in vivo optimization algorithm called orthokinetic GSA (OGSA) is proposed to carry out the TST. Furthermore, to determine the direction of the orthokinetic agent movement in every iteration of the operation, we propose several strategies according to the fitness ranking of the nanoswimmers in the BGF. To verify the superiority of the OGSA and choose the optimal evolution strategy, some numerical experiments are presented and compared with that of the brute-force search, which represents the traditional method for TST. It is found that the TST performance can be improved by the weak priority evolution strategy (WP-ES) in most of the scenarios [ABSTRACT FROM AUTHOR]

Cooperative co-evolutionary algorithms have addressed many large-scale problems successfully, but the nonseparable large-scale problems with overlapping subcomponents are still a serious difficulty that has not been conquered yet. First, the existence of shared variables makes the problem hard to be decomposed. Second, existing cooperative co-evolutionary frameworks usually cannot maintain the two crucial factors: high cooperation frequency and effective computing resource allocation, simultaneously when optimizing the overlapping subcomponents. Aiming at these two issues, this article proposes a new contribution-based cooperative co-evolutionary algorithm to decompose and optimize nonseparable large-scale problems with overlapping subcomponents effectively and efficiently: 1) a contribution-based decomposition method is proposed to assign the shared variables. Among all the subcomponents containing a shared variable, the one that contributes the most to the entire problem will include the shared variable and 2) to achieve the two crucial factors at the same time, a new contribution-based optimization framework is designed to award the important subcomponents based on the round-robin structure. Experimental studies show that the proposed algorithm performs significantly better than the state-of-the-art algorithms due to the effective grouping structure generated by the proposed decomposition method and the fast optimizing speed provided by the new optimization framework. [ABSTRACT FROM AUTHOR]

The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is a need for an in-depth exploration of integrating the microscopic speed control of CAVs with the macroscopic variable speed limit (VSL) of human-driven vehicles (HDVs). This paper proposes a Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME) framework, which combines microscopic CAV control with macroscopic VSL control. The framework incorporates a Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework for intelligent decision-making by microscopic-level vehicles. Additionally, an evolutionary strategy is developed to design the VSL network architecture, enabling macroscopic level real-time speed limit adjustments based on infrastructure. A multi-objective reward function is proposed to optimize both micro and macro efficiency and safety, accounting for both vehicle behavior and traffic flow. Experiments on the designed Bottleneck traffic scenarios show that the proposed approach, CTO-ME, is able to achieve superior performance and outperforms other baselines in terms of traffic throughput, average speed, and safety. Specifically, CTO-ME enhances average velocity by 37%, increases overall throughput by 309%, and raises arrival ratio by 70% than traditional Intelligent Driver Model (IDM). • Macro–Micro Control Integration: We tackled the challenge of harmonizing macroscopic (Variable Speed Limit - VSL) and microscopic (Connected and Automated Vehicles - CAVs) controls to enhance both granular and overarching traffic efficiency. Our proposed method, the Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME), cooperates with these control methods, paving the way for synchronized and efficient traffic management. • We integrated the Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework to amplify the decision-making capabilities of vehicles and developed an advanced evolutionary strategy for VSL control to facilitate real-time speed limit adjustments. • We proposed a multi-objective reward function, considering both micro and macro efficiency and safety considerations, to promote optimized traffic flow and enhance overall control performance. [ABSTRACT FROM AUTHOR]

The class of algorithms called Hessian Estimation Evolution Strategies (HE-ESs) update the covariance matrix of their sampling distribution by directly estimating the curvature of the objective function. The approach is practically efficient, as attested by respectable performance on the BBOB testbed, even on rather irregular functions. In this article, we formally prove two strong guarantees for the (1 + 4)-HE-ES, a minimal elitist member of the family: stability of the covariance matrix update, and as a consequence, linear convergence on all convex quadratic problems at a rate that is independent of the problem instance. [ABSTRACT FROM AUTHOR]

Solar photovoltaic (PV) is the most rapidly expanding renewable resource worldwide. Yet, its full potential may be hindered by mismatches with market demand and correlated production profiles. In this research, we explore a case study of innovative PV placements in alpine regions using two, soft-linked optimization models of Switzerland's electricity system. Using Swissmod, an electricity dispatch and load-flow model, and OREES, an electricity system model employing evolution strategy to optimize PV placement, we simulate market prices of optimized PV placements given multiple years of weather data, various CO2 prices, and considering future electricity infrastructure developments across Europe. Mountain placements result in higher market value and less required area relative to lower-altitude PV placement strategies. The higher market value is driven by better alignment with demand, particularly during winter when demand is highest. We found that optimized alpine placements offer revenues of panel capacity (EUR/kW/year) that are on average 20% higher than revenues from urban PV installations. Furthermore, the Swiss mountains could host more than 1 GW of capacity with even greater revenues (33%). Alpine PV installations, with their higher market values and increased value factors, can potentially be very profitable investments and are also valuable from a system perspective. [ABSTRACT FROM AUTHOR]

Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass industry, paper industry, appliances industry, and pharmaceutical industry, and the generation of the best schedule is very important for these manufacturing systems. Evolution Strategy (ES) is a subclass of Evolutionary algorithms and in this paper, we propose an Improved Evolution Strategy to reduce the makespan of PFSSP. Two variants of the Improved Evolution Strategy are proposed namely ES5 and ES10. The initial solution is generated using the shortest processing time rule. In ES5, four offsprings are generated from one parent while in ES10, nine offsprings are generated from one parent. The selection pool consists of both the parents and offsprings. Quad swap mutation operator has been proposed to minimize computational time and for the maximum search of solution space. Also, a variable mutation rate is used for the fine-tuning of results, with the increasing number of iterations the mutation rate is reduced. The performances of both ES variants were tested on two test domains. First, it is applied to benchmark the PFSSP of Carlier and Reeves. Computational results are matched with other well-known techniques available in the literature, and the results show the effectiveness and robustness of the proposed techniques. Secondly, ES is applied to the real-life problem for the manufacturing of batteries to demonstrate its effectiveness. Data was taken from Pakistan Accumulator for NS30-40 Plates battery, the company is daily producing 1400 units of NS30-40 Plates battery. ES is applied to different batch sizes i.e. 35, 140, 1120 & 1400. Our results show that a Min %GAP of 1.25 is found using ES10. Hence the company can increase monthly 450 units of NS30 batteries using the ES10 algorithm.

In times of climate change, growing world population, and the resulting scarcity of resources, efficient and economical usage of agricultural land is increasingly important and challenging at the same time. To avoid disadvantages of monocropping for soil and environment, it is advisable to practice intercropping of various plant species whenever possible. However, intercropping is challenging as it requires a balanced planting schedule due to individual cultivation time frames. Maintaining a continuous harvest throughout the season is important as it reduces logistical costs and related greenhouse gas emissions, and can also help to reduce food waste. Motivated by the prevention of food waste, this work proposes a flexible optimization method for a full harvest season of large crop ensembles that complies with given economical and environmental constraints. Our approach applies evolutionary algorithms and we further combine our evolution strategy with a sophisticated hierarchical loss function and adaptive mutation rate. We thus transfer the multi-objective into a pseudo-single-objective optimization problem, for which we obtain faster and better solutions than those of conventional approaches.

Path integral policy improvement (PI2) is known to be an efficient reinforcement learning algorithm, particularly, if the target system is a high-dimensional dynamical system. However, PI2, and its existing extensions, have adjustable parameters, on which the efficiency depends significantly. This article proposes an extension of PI2 that adjusts all of the critical parameters automatically. Motion acquisition tasks for three different types of simulated legged robots were performed to test the efficacy of the proposed algorithm. The results show that the proposed method cannot only eliminate the burden on the user to set the parameters appropriately but also improve the optimization performance significantly. For one of the acquired motions, a real robot experiment was conducted to show the validity of the motion. [ABSTRACT FROM AUTHOR]

Cardiopulmonary resuscitation (CPR) is one of the most critical emergency interventions for sudden cardiac arrest. In this paper, a robust sinusoidal model-fitting method based on a Evolution Strategy inspired algorithm for CPR quality parameters – naming chest compression frequency and depth – as measured by an inertial measurement unit (IMU) attached to the wrist is presented. The proposed approach will allow bystanders to improve CPR as part of a continuous closed-loop support system once integrated into a smartphone or smartwatch application. By evaluating the model's precision with data recorded by a training mannequin as reference standard, a variance for the compression frequency of ± 2.22 compressions per minute (cpm) has been found for the IMU attached to the wrist. It was found that this previously unconsidered position and thus, the use of smartwatches is a suitable alternative to the typical placement of phones in hand for CPR training. [ABSTRACT FROM AUTHOR]