Hybrid Selection Based Multi/Many-Objective Evolutionary Algorithm

In the last decade, numerous Multi/Many-Objective Evolutionary Algorithms (MOEAs) have been proposed to handle Multi/Many-Objective Problems (MOPs) with challenges such as discontinuous Pareto Front (PF), degenerate PF, etc. MOEAs in the literature can be broadly divided into three categories based on the selection strategy employed such as dominance, decomposition, and indicator-based MOEAs. Each category of MOEAs have their advantages and disadvantages when solving MOPs with diverse characteristics. In this work, we propose a Hybrid Selection based MOEA, referred to as HS-MOEA, which is a simple yet effective hybridization of dominance, decomposition and indicator-based concepts. In other words, we propose a new environmental selection strategy where the Pareto-dominance, reference vectors and an indicator are combined to effectively balance the diversity and convergence properties of MOEA during the evolution. The superior performance of HS-MOEA compared to the state-of-the-art MOEAs is demonstrated through experimental simulations on DTLZ and WFG test suites with up to 10 objectives.

Hybrid Selection Method of Feature Variables and Prediction Modeling for Municipal Solid Waste Incinerator Temperature

It is difficult to establish an accurate mechanism model for prediction incinerator temperatures due to the comprehensive complexity of the municipal solid waste (MSW) incineration process. In this paper, feature variables of incineration temperature are selected by combining with mutual information (MI), genetic algorithms (GAs) and stochastic configuration networks (SCNs), and the SCN-based incinerator temperature model is obtained simultaneously. Firstly, filter feature selection is realized by calculating the MI value between each feature variable and the incinerator temperature from historical data. Secondly, the fitness function of GAs is defined by the root mean square error of the incinerator temperature obtained by training SCNs, and features obtained by MI methods are searched iteratively to complete the wrapper feature selection, where the SCN-based incinerator temperature prediction model is obtained. Finally, the proposed model is verified by MSW incinerator temperature historical data. The results show that the SCN-based prediction model using the hybrid selection method can better predict the change trend of incinerator temperature, which proves that the SCNs has great development potential in the field of prediction modeling.

Cancer Cell Fusion and Post-Hybrid Selection Process (PHSP)

Fusion of cancer cells either with other cancer cells (homotypic fusion) in local vicinity of the tumor tissue or with other cell types (e.g., macrophages, cancer-associated fibroblasts (CAFs), mesenchymal stromal-/stem-like cells (MSC)) (heterotypic fusion) represents a rare event. Accordingly, the clinical relevance of cancer-cell fusion events appears questionable. However, enhanced tumor growth and/or development of certain metastases can originate from cancer-cell fusion. Formation of hybrid cells after cancer-cell fusion requires a post-hybrid selection process (PHSP) to cope with genomic instability of the parental nuclei and reorganize survival and metabolic functionality. The present review dissects mechanisms that contribute to a PHSP and resulting functional alterations of the cancer hybrids. Based upon new properties of cancer hybrid cells, the arising clinical consequences of the subsequent tumor heterogeneity after cancer-cell fusion represent a major therapeutic challenge. However, cellular partners during cancer-cell fusion such as MSC within the tumor microenvironment or MSC-derived exosomes may provide a suitable vehicle to specifically address and deliver anti-tumor cargo to cancer cells.

Providing an Adaptive Routing along with a Hybrid Selection Strategy to Increase Efficiency in NoC-Based Neuromorphic Systems

Effective and efficient routing is one of the most important parts of routing in NoC-based neuromorphic systems. In fact, this communication structure connects different units through the packets routed by routers and switches embedded in the network on a chip. With the help of this capability, not only high scalability and high development can be created, but by decreasing the global wiring to the chip level, power consumption can be reduced. In this paper, an adaptive routing algorithm for NoC-based neuromorphic systems is proposed along with a hybrid selection strategy. Accordingly, a traffic analyzer is first used to determine the type of local or nonlocal traffic depending on the number of hops. Then, considering the type of traffic, the RCA and NoP selection strategies are used for the nonlocal and local strategies, respectively. Finally, using the experiments that performed in the simulator environment, it has been shown that this solution can well reduce the average delay time and power consumption.

Contemporary Direction of Eggplant Selection for the South of Russia

Eggplant selection is crucial for the south of Russia due to the concentration of production and processing capacities in this region. When selecting eggplants, regional characteristics, in particular specific stressors, should be taken into account. The main stressors are low temperatures in the initial period of plant growth and development, sharp changes in soil and air temperature and humidity during the growing season, high temperatures during crop formation, and harmful fungal and mycoplasma diseases. It is possible to reduce the dependence on imports by developing production in spring greenhouses, which will extend the consumption period by 3-4 months. This requires varieties and hybrids with specific properties: high productivity, high adaptability, compact habit of plants, lack of thorns, low pubescence, etc. F1 hybrids combining high productivity with adaptability and product quality can grow in greenhouses. When collecting linear materials for creating hybrids, it is advisable to use hard provocative backgrounds with a subsequent assessment of the combinational ability. This research aimed to develop new varieties and hybrids of eggplant for the south of Russia. The study was conducted in the Rostov Selection and Seed Center in 2010-2019 on fields and in spring greenhouses. Materials from different regions of the world were collected. Varieties and hybrids were evaluated by their valuable traits and properties. Linear materials were created for sources and donors of characters that are in demand. The collection of the best varieties was involved in selection programs for creating an assortment for the south of Russia. More than 10 eggplant varieties and hybrids with high productivity and adaptive qualities were obtained. Keywords: eggplant, source material, variety, hybrid, selection, signs, applications, stability, heterosis