Evolution Strategy Research Articles

Fungal microtubule organizing centers are evolutionarily unstable structures

For most Eukaryotic species the requirements of cilia formation dictate the structure of microtubule organizing centers (MTOCs). In this study we find that loss of cilia corresponds to loss of evolutionary stability for fungal MTOCs. We used iterative search algorithms to identify proteins homologous to those found in saccharomyces cerevisiae, and schizosaccharomyces pombe MTOCs, and calculated site-specific rates of change for those proteins that were broadly phylogenetically distributed. Our results indicate that both the protein composition of MTOCs as well as the sequence of MTOC proteins are poorly conserved throughout the fungal kingdom. To begin to reconcile this rapid evolutionary change with the rigid structure and essential function of the s. cerevisiae MTOC we further analyzed how structural interfaces among proteins influence the rates of change for specific residues within a protein. We find that a more stable protein may stabilize portions of an interacting partner where the two proteins are in contact. In summary, while the protein composition and sequences of the MTOC may be rapidly changing the proteins within the structure have a stabilizing effect on one another. Further exploration of fungal MTOCs will expand our understanding of how changes in the functional needs of a cell have affected physical structures, proteomes, and protein sequences throughout fungal evolution.

Neuroevolution reinforcement learning for multi-echelon inventory optimization with delivery options and uncertain discount

The advanced information technology has enabled supply chain to make centralized optimal decision, allowing to make a global optimal solution. However, dealing with uncertainty is important in inventory management. Besides demand and supply uncertainties, supplier discounts also often arise unexpectedly. Further, suppliers or third-parties typically offer various delivery options in which trade-off occurs between cost and lead time. Thus, this study introduces new problem namely Multi-Echelon Inventory Optimization with Delivery Options and Uncertain Discount (MEIO-DO-UD). As a solution, Neuroevolution Reinforcement Learning (NERL) framework is developed for minimizing total system cost. The environment is modeled via System Dynamics (SD) and actor is presented by integration of Artificial Neural Network and Evolutionary Algorithm (EA), creating an effective decision-making model under dynamic uncertainty. The experimental study has been conducted where two different supply chain networks are given namely serial and divergence. Three EA algorithms are compared namely Differential Evolution (DE), Memetic Algorithm (MA), and Evolution Strategy (ES). Furthermore, NERL is also compared with the EA-optimized classical continuous review model namely (s,Q). The result shows that regardless what EA type is used, the proposed NERL always outperforms EA-optimized (s,Q) model. The more complex the problem, the further improvement can be made i.e. cost reduction up to 58%, followed by the fill rate improvement. The result also shows that NERL can avoid overfitting. Managerial implications are highlighted where NERL provides the more stable inventory level among all supply chain partners and bull-whip effect can be damped.

Who is a Leader in the Leading Eight? Indirect Reciprocity under Private Assessment

Indirect reciprocity is a mechanism that explains large-scale cooperation in human societies. In indirect reciprocity, an individual chooses whether to cooperate with another based on reputation information, and others evaluate the action as good or bad. Under what evaluation rule (called “social norm”) cooperation evolves has long been of central interest in the literature. It has been reported that if individuals can share their evaluations (i.e., public reputation), social norms called “leading eight” can be evolutionarily stable. On the other hand, when they cannot share their evaluations (i.e., private assessment), the evolutionary stability of cooperation is still in question. To tackle this question, we create a novel method to analyze the reputation structure in the population under private assessment. Specifically, we characterize each individual by two variables, “goodness” (what proportion of the population considers the individual as good) and “self-reputation” (whether an individual thinks of him or herself as good or bad), and analyze the stochastic process of how these two variables change over time. We discuss the evolutionary stability of each of the leading-eight social norms by studying the robustness against invasions of unconditional cooperators and defectors. We identify key pivots in those social norms for establishing a high level of cooperation or stable cooperation against mutants. Our finding gives an insight into how human cooperation is established in a real-world society. Published by the American Physical Society 2024

PHYLOES an evolution strategy approach phylogenetic inference based on the balanced minimum evolution criterion

PHYLOES an evolution strategy approach phylogenetic inference based on the balanced minimum evolution criterion

The Digital Evolution Strategies for Overcoming Cybersecurity and Adoption Challenges in French SMEs

French Small and Medium-sized Enterprises (SMEs) face a crucial juncture in their path towards digital transformation. They encounter notable obstacles in embracing digital technologies and bolstering cybersecurity measures. This integrative literature review examines the complex relationship between digitalization and cybersecurity, highlighting their importance for enterprises' ongoing competitiveness and operational security. The study is focused on two primary areas: the integration of digital technologies and the establishment of robust cybersecurity frameworks. This review thoroughly examines existing research to offer strategic insights for leaders of French SMEs and IT policymakers. It employs rigorous inclusion and exclusion criteria, detailed data extraction, and comprehensive synthesis to ensure a systematic analysis. The findings emphasize the critical roles of organizational culture and innovation diffusion in shaping the adoption of technology and the implementation of security practices. The review puts forward practical suggestions to address the digital divide, strengthen cybersecurity readiness, and promote a digital environment accessible to all sectors of French SMEs. These recommendations focus on enhancing operational efficiency, minimizing expenses, and fortifying the security framework to bolster a solid national digital transformation. The study's conclusions highlight the significance of these findings for future research and practice, providing a thorough guide for incorporating digital transformation and cybersecurity in the context of French SMEs.

Combining evolutionary strategies and novelty detection to go beyond the alignment limit of the Z3 3HDM

We present a novel artificial intelligence approach for beyond the Standard Model parameter space scans by augmenting an evolutionary strategy with novelty detection. Our approach leverages the power of evolutionary strategies, previously shown to quickly converge to the valid regions of the parameter space, with a novelty reward to continue exploration once converged. Taking the Z3 3HDM as our physics case, we show how our methodology allows us to quickly explore highly constrained multidimensional parameter spaces, providing up to eight orders of magnitude higher sampling efficiency when compared with pure random sampling and up to four orders of magnitude when compared to random sampling around the alignment limit. In turn, this enables us to explore regions of the parameter space that have been hitherto overlooked, leading to the possibility of novel phenomenological realizations of the Z3 three Higgs doublet model that had not been considered before. Published by the American Physical Society 2024

Systems and Methods for Continuous Evolution of Enzymes.

Directed evolution generates novel biomolecules with desired functions by iteratively diversifying the genetic sequence of wildtype biomolecules, relaying the genetic information to the molecule with function, and selecting the variants that progresses towards the properties of interest. While traditional directed evolution consumes significant labor and time for each step, continuous evolution seeks to automate all steps so directed evolution can proceed with minimum human intervention and dramatically shortened time. A major application of continuous evolution is the generation of novel enzymes, which catalyze reactions under conditions that are not favorable to their wildtype counterparts, or on altered substrates. The challenge to continuously evolve enzymes lies in automating sufficient, unbiased gene diversification, providing selection for a wide array of reaction types, and linking the genetic information to the phenotypic function. Over years of development, continuous evolution has accumulated versatile strategies to address these challenges, enabling its use as a general tool for enzyme engineering. As the capability of continuous evolution continues to expand, its impact will increase across various industries. In this review, we summarize the working mechanisms of recently developed continuous evolution strategies, discuss examples of their applications focusing on enzyme evolution, and point out their limitations and future directions.

Evolutionary game analysis of data sharing among large and medium-sized enterprises in the perspective of platform empowerment

With the swift advancement of the global digital economy, data has emerged as a critical component in fostering the integration of large enterprises with small and medium-sized enterprises (SMEs). Nevertheless, due to disparities in resources and capabilities between these entities, there is a deficiency in the willingness to share data, hindering the full actualization of data’s potential value. Hence, it is imperative to facilitate a novel cooperative development paradigm wherein platforms enable data sharing among large enterprises and SMEs. In this paper, we construct a tripartite evolutionary game model encompassing large enterprises, SMEs, and digital platforms, examine the evolutionary stable strategies adopted by these entities in the data sharing, and use numerical simulation to explore the system’s evolutionary stability under various influencing factors. Contrasting with prior research, this study considers the heterogeneity of enterprise scale and delves into the data sharing dynamics between large enterprises and SMEs. Simultaneously, it positions the digital platform as a player in the game, examining its impact on data sharing among the enterprises. Findings indicate that: (1) SMEs exhibit greater eagerness for data sharing compared to large enterprises, which display a U-shaped influence during the process; (2) Digital platforms are particularly sensitive to costs, with the platform’s initiative and the service quality will affect enterprises strategic choices; (3) Government subsidies positively encourage tripartite cooperation, and robust data security governance framework is crucial for enterprises. Finally, based on the results of the study and combining with the current situation of digital economy development, it puts forward the suggestions for promoting platforms to empower large enterprises and SMEs to realize data sharing and the prospects for future research.

A Translation-Independent Directed Evolution Strategy to Engineer Aminoacyl-tRNA Synthetases

A Translation-Independent Directed Evolution Strategy to Engineer Aminoacyl-tRNA Synthetases

Continuous optimization by quantum adaptive distribution search

We introduce the quantum adaptive distribution search (QuADS), a quantum continuous optimization algorithm that integrates Grover adaptive search (GAS) with the covariance matrix adaptation evolution strategy (CMA-ES), a classical technique for continuous optimization. QuADS utilizes the quantum-based search capabilities of GAS and enhances them with the principles of CMA-ES for more efficient optimization. It employs a multivariate normal distribution for the initial state of the quantum search and repeatedly updates it throughout the optimization process. Our numerical simulations show that QuADS outperforms both GAS and CMA-ES. This is achieved through adaptive refinement of the initial state distribution rather than consistently using a uniform state, resulting in fewer oracle calls. This study presents an important step toward exploiting the potential of quantum computing for continuous optimization. Published by the American Physical Society 2024

An enhanced sparrow search swarm optimizer via multi-strategies for high-dimensional optimization problems

With the development of science and technology, high-dimensional global optimization problems have become increasingly prevalent for scientific research and engineering, such as gene recognition, vehicle routing, job scheduling, and network topology. These problems are typically characterized by enormous and complex search spaces and numerous local minima, making it challenging to find the global optimal solution with limited computing resources. This paper introduces an enhanced sparrow search swarm optimizer (ESSSO) based on a bio-mimetic method. The ESSSO employs an adaptive sinusoidal walk strategy based on the von Mises distribution, a learning strategy utilizing roulette wheel selection, a two-stage evolution strategy, and a selection mutation strategy to address these issues. The proposed sinusoidal walk strategy, grounded in the von Mises distribution, supports a balanced evolutionary search. This mechanism disperses the individuals in a swarm in various directions based on a circular normal distribution. It then leads the search and adaptively adjusts their step sizes according to the size of the search domain during each generation of evolution. The learning strategy, based on roulette wheel selection, enhances the diversity of the population and improves the global search capability of the algorithm during the initial iterations. The two-stage evolution strategy involves a sine-learning mechanism based on the von Mises distribution and an adaptive mutation mechanism. The former is designed to boost the convergence speed of ESSSO, while the latter prevents ESSSO from getting trapped in a local optimum. Additionally, the selection mutation strategy further enhances convergence speed while maintaining population diversity. These strategies promote exploration in the early stages of evolution and exploitation in the later stages, enabling a well-balanced search for optimal solutions. We conducted comprehensive experiments two standard benchmark sets (i.e., CEC2010 and CEC2013), antenna array optimization, feature selection, and four engineering design problems. The results indicate that ESSSO outperforms ten comparison algorithms, especially in scenarios with smaller population sizes. This confirms its effectiveness in high-dimensional global optimization tasks and demonstrates that it can achieve better results with less computational resource consumption.

Evolutionary Game Analysis of Governments’ and Enterprises’ Carbon-Emission Reduction

With the increasingly serious problem of global climate change, many countries are positively promoting carbon-emission-reduction actions. In order to deeply explore the interaction between enterprises’ carbon-emission reduction and governments’ regulation, this paper builds evolutionary game models between governments and enterprises under the reward-and-punishment mechanism. The peer-incentive mechanism is introduced to incentivize enterprises to reduce carbon emissions and coordinate governments and enterprises. The evolutionary-stability strategies are obtained by solving the evolutionary game models. The stability of equilibrium points under different situations is theoretically and numerically studied. The results show that the existence of peer incentives makes enterprises more inclined to positively reduce carbon emissions and governments more inclined to positively regulate. A sufficiently large peer fund can always encourage enterprises to choose positive carbon-reduction emission strategies, while governments choose positive regulation strategies. Not only the increasing rewards and fines but also lowering regulatory costs will promote carbon-emission-reduction behaviors of enterprises. Peer incentives are more effective in promoting positive emission reduction of enterprises compared with rewards and punishments. This study can provide important guidance for governments to formulate regulatory strategies and for enterprises to formulate emission-reduction strategies.

The impact of ciliate nutrition on aquatic ecosystems

Abstract Ciliate interactions including mode of nutrition contribute significantly to freshwater and marine ecosystems. Ciliates associations with their bacterial communities, and symbionts have crucial contributions to biomass productivity, transfer of nutrients to higher trophic levels and thus to sustain the ecosystems. The mixotrophic and heterotrophic ciliates are crucial in nutrient cycling by increasing nitrogen, phosphorus, and sulphur. Bacterial predator ciliates regulate the bacterial population and function as bioindicators. Endosymbionts in ciliates illustrate the incorporation of photosynthesis, nitrogen fixation and recycling, and methanogenesis, into ciliate hosts by endosymbionts. Endosymbiosis represents an evolutionary strategy of protists to acquire novel biochemical functions. Herein, I briefly give an account for the impact of ciliate nutrition on aquatic systems.

Adaptive attenuation of virulence in hypervirulent carbapenem-resistant Klebsiella pneumoniae.

The emergence of nosocomial infections caused by hypervirulent and carbapenem-resistant K. pneumoniae (hv-CRKP) has become a significant public health challenge. The genetic traits of virulence and resistance plasmids in hv-CRKP have been extensively studied; however, research on the adaptive evolution strategies of clinical strains inside the host was scarce. This study aimed to understand the effects of antibiotic treatment on the phenotype and genotype characteristics of hv-CRKP. We investigated the evolution of hv-CRKP strains isolated from the same patient to elucidate the transition between hospital invasion and colonization. A comparative genomics analysis was performed to identify single nucleotide polymorphisms in the rmpA promoter. Subsequent validation through RNA-seq and gene deletion confirmed that distinct rmpA promoter sequences exert control over the mucoid phenotype. Additionally, biofilm experiments, cell adhesion assays, and animal infection models were conducted to illuminate the influence of rmpA promoter diversity on virulence changes. We demonstrated that the P12T and P11T promoters of rmpA possess strong activity, which leads to the evolution of CRKP into infectious and virulent strains. Meanwhile, the specific sequence of polyT motifs in the rmpA promoter led to a decrease in the lethality of hv-CRKP and enhanced cell adhesion and colonization. To summarize, the rmpA promoter of hv-CRKP is utilized to control capsule production, thereby modifying pathogenicity to better suit the host's ecological environment.IMPORTANCEThe prevalence of hospital-acquired illness caused by hypervirulent carbapenem-resistant Klebsiella pneumoniae (hv-CRKP) is significant, leading to prolonged antibiotic treatment. However, there are few reports on the phenotypic changes of hv-CRKP in patients undergoing antibiotic treatment. We performed a comprehensive examination of the genetic evolutionary traits of hv-CRKP obtained from the same patient and observed variations in the promoter sequences of the virulence factor rmpA. The strong activity of the promoter sequences P11T and P12T enhances the consistent production of capsule polysaccharides, resulting in an invasive strain. Conversely, weak promoter activity of P9T and P10T is advantageous for exposing pili, hence improving bacterial cell attachment ability and facilitating bacterial colonization. This finding also explains the confusion of some clinical strains carrying wild-type rmpA but exhibiting a low mucoid phenotype. This adaptive alteration facilitates the dissemination of K. pneumoniae within the hospital setting.

Analysis of Multi-Stakeholder Behavioral Strategies in the Construction and Demolition Waste Recycling Industry through an Evolutionary Game Theory

Construction and demolition waste (C&DW) recycling products have drawn worldwide attention over the past few decades. There is a general agreement among researchers that C&DW recycling is an important means for curbing the deterioration of the environment. Previous papers mainly focused on the decision-making behavior of dual stakeholders or tripartite stakeholders, as well as the lack of cooperation among multiple stakeholders. This study explored a dynamic evolutionary game model with three different parameter conditions to research the decision-making behaviors and stable strategies of the multi-stakeholders involved in the C&DW recycling product industry, including government departments and some enterprises. This research also investigated how the government’s supervision costs, penalties applied to enterprises, and resource taxes affect the dynamic evolution process of C&DW recycling. This research conducted numerical simulations using Python to analyze stakeholders’ behavioral evolutionarily stable strategy (ESS) and the sensitivity to main factors in each stage of the C&DW recycling process to accelerate the development of construction waste resource utilization. Based on the results of the evolutionary game, this paper proposed that the roles of multi-stakeholders are different at different stages of industry development, and that supervision costs, penalties, and resource tax have their own impacts on the C&DW recycling product industry. The paper suggests a range of discussions and simulation studies to highlight the significance of the government’s refined and moderate adjustments to the regulatory incentive system and the level of government regulation and involvement at different stages of the process. These adjustments are aimed at promoting the sustainable recycling and utilization of construction and demolition waste (C&DW) products within some countries’ construction industry.

Unlocking the biosynthetic potential and taxonomy of the Antarctic microbiome along temporal and spatial gradients.

Extreme environments, such as Antarctica, select microbial communities that display a range of evolutionary strategies to survive and thrive under harsh environmental conditions. These include a diversity of specialized metabolites, which have the potential to be a source for new natural product discovery. Efforts using (meta)genome mining approaches to identify and understand biosynthetic gene clusters in Antarctica are still scarce, and the extent of their diversity and distribution patterns in the environment have yet to be discovered. Herein, we investigated the biosynthetic gene diversity of the biofilm microbial community of Whalers Bay, Deception Island, in the Antarctic Peninsula and revealed its distribution patterns along spatial and temporal gradients by applying metagenome mining approaches and multivariable analysis. The results showed that the Whalers Bay microbial community harbors a great diversity of biosynthetic gene clusters distributed into seven classes, with terpene being the most abundant. The phyla Proteobacteria and Bacteroidota were the most abundant in the microbial community and contributed significantly to the biosynthetic gene abundances in Whalers Bay. Furthermore, the results highlighted a significant correlation between the distribution of biosynthetic genes and taxonomic diversity, emphasizing the intricate interplay between microbial taxonomy and their potential for specialized metabolite production.IMPORTANCEThis research on antarctic microbial biosynthetic diversity in Whalers Bay, Deception Island, unveils the hidden potential of extreme environments for natural product discovery. By employing metagenomic techniques, the research highlights the extensive diversity of biosynthetic gene clusters and identifies key microbial phyla, Proteobacteria and Bacteroidota, as significant contributors. The correlation between taxonomic diversity and biosynthetic gene distribution underscores the intricate interplay governing specialized metabolite production. These findings are crucial for understanding microbial adaptation in extreme environments and hold significant implications for bioprospecting initiatives. The study opens avenues for discovering novel bioactive compounds with potential applications in medicine and industry, emphasizing the importance of preserving and exploring these polyextreme ecosystems to advance biotechnological and pharmaceutical research.

An evolutionary game and system dynamics approach for the production and consumption of carbon-labeled products-based on a media monitoring perspective

Carbon label is acknowledged as an effective way to combat the problem of global warming. As a powerful way to encourage individuals to adjust their consumption patterns and to promote the development of green consumption, carbon-labeled products are widely developed in China. To reveal the production and consumption process of carbon-labeled products, the present study constructs a tripartite game model consisting of the government, carbon-labeled products manufacturers and consumers based on a media monitoring perspective. The evolutionary stability strategy (ESS) is firstly determined by solving the replication dynamic equations and stability analysis of the equilibrium point, followed by the strategy analysis and sensitivity analysis through numerical simulation. The results show that media supervision can effectively complement and constrain government supervision. In addition, it can promote enterprises to standardize production and enhance consumers' trust and willingness to buy carbon-labeled products. The introduction of media supervision can well realize the ideal equilibrium of "effective government supervision, enterprise compliance and consumer support for purchase".

IMPI: An Interface for Low-Frequency Point Mutation Identification Exemplified on Resistance Mutations in Chronic Myeloid Leukemia

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs.

Self-tuning speed and flow control of micro turbojet engines based on an improved evolutionary strategy

At present, the controllable parameters of micro turbojet engines in engineering applications are mainly speed-fuel flow (hereinafter referred to as flow) control, in which closed-loop proportional–integral–derivative (PID) control is mostly used to achieve a stable control of engine speed under slow engine conditions. For the optimal adjustment of PID parameters, this paper designs an improved evolutionary strategy for the self-tuning of control parameters in the engine speed and flow control system and formulates an improved PI controller based on a neural network. The simulation experimental results show that the method can realistically achieve stable and fast control of the engine under above slow conditions.

Trust region framework-based design of sub-6 GHz m-MIMO antenna and evaluation of SAR

Purpose Currently, massive multiple-input multiple-output (m-MIMO) antennas are typically designed using complex trial-and-error methods. The purpose of this study is to determine an effective optimization method to achieve more efficient antenna design processes. Design/methodology/approach This paper presents the design stages of a m-MIMO antenna array compatible with 5G smartphones operating in long term evolution (LTE) bands 42, 43 and 46, based on a specific algorithm. Each antenna element in the designed 10-port m-MIMO antenna array is intended to perfectly cover the three specified LTE bands. The optimization methods used for this purpose include the Nelder–Mead simplex algorithm, covariance matrix adaptation evolution strategy, particle swarm optimization and trust region framework (TRF). Findings Among the primary optimization algorithms, the TRF algorithm met the defined objectives most effectively. The achieved antenna efficiency values exceeded 60.81% in the low band and 68.39% in the high band, along with perfect coverage of the desired bands, demonstrating the success of the design with the TRF algorithm. In addition, the potential electromagnetic field exposure caused by the designed m-MIMO antenna array is elaborated upon in detail using computational human models through specific absorption rate analysis. Originality/value The comparison of four different algorithms (two local and two global) for use in the design of a 10-element m-MIMO antenna array with a complex structural configuration and the success of the design implemented with the selected algorithm distinguish this study from others.