Evolution Strategy Research Articles

Genomic insights into the cellular specialization of predation in raptorial protists

BackgroundPredation is a fundamental mechanism for organisms to acquire energy, and various species have evolved diverse tools to enhance their hunting abilities. Among protozoan predators, raptorial Haptorian ciliates are particularly fascinating as they possess offensive extrusomes known as toxicysts, which are rapidly discharged upon prey contact. However, our understanding of the genetic processes and specific toxins involved in toxicyst formation and discharge is still limited.ResultsIn this study, we investigated the predation strategies and subcellular structures of seven Haptoria ciliate species and obtained their genome sequences using single-cell sequencing technology. Comparative genomic analysis revealed distinct gene duplications related to membrane transport proteins and hydrolytic enzymes in Haptoria, which play a crucial role in the production and discharge of toxicysts. Transcriptomic analysis further confirmed the abundant expression of genes related to membrane transporters and cellular toxins in Haptoria compared to Trichostomatia. Notably, polyketide synthases (PKS) and l-amino acid oxidases (LAAO) were identified as potentially toxin genes that underwent extensive duplication events in Haptoria.ConclusionsOur results shed light on the evolutionary and genomic adaptations of Haptorian ciliates for their predation strategies in evolution and provide insights into their toxic mechanisms.

Unveiling Agricultural Shifts through Stakeholder Interactions and Strategic Dynamics in Green Rice Production

This study explores the strategic interactions among the government, growers, and the public within the context of green rice production, employing an evolutionary game theory framework. Recognizing the intricate dynamics of agricultural sustainability, we construct a three-party evolutionary game model to investigate the strategic decision-making processes and stability conditions of each stakeholder. The model assesses how various strategies evolve under the influence of economic incentives, regulatory measures, and public engagement. Through analytical and numerical methods, including stability analysis and MATLAB 2020b simulations, we identify the Evolutionarily Stable Strategies (ESS) that signify sustainable practices in green rice production. The results reveal that as government incentives for green production increase and fines for non-green practices are enforced, the likelihood of growers adopting sustainable practices significantly rises. Furthermore, the strategic enhancement of economic incentives and reputational factors not only bolsters governmental regulatory commitment but also reduces the necessity for public supervision, demonstrating a systemic shift towards self-regulation and market-driven sustainability. The simulations demonstrate the system’s evolution towards a stable state where governmental regulation is fully enforced, growers adopt green production universally, and public supervision becomes redundant. The findings underscore the importance of designing policy interventions that harmonize economic and environmental objectives, suggesting that well-structured incentives and regulatory measures can catalyze the transition towards sustainable agricultural practices. Additionally, our study highlights the pivotal role of establishing effective incentive structures that ensure the economic benefits of green production outweigh the costs, facilitating an autonomous regulatory system. This study contributes to the understanding of how strategic interactions shaped by policy and market forces can foster agricultural sustainability.

An evolution strategy with tailor-made mutation operator for colored balanced traveling salesman problem

An evolution strategy with tailor-made mutation operator for colored balanced traveling salesman problem

Evolutionary game and system dynamics for analysis on stakeholder strategies of regional high-speed rail project in investment decision stage

This paper studies the strategic choices under the conflict among stakeholders of regional high-speed rail project at the investment decision stage in the context of the ‘high-speed rail controversy’. This study develops a tripartite evolutionary game model among the central government, the local government and China Railway, analyzes the evolutionary stability strategy, discusses the influence of multiple factors on the choice of tripartite strategy, and further calculates and analyzes the stability of the equilibrium point in the tripartite evolutionary game system. Then the dynamic change trend of strategy choice brought by the change of each factor is investigated through system dynamics simulation. The results show that the lower the intervention cost and reward to the other two parties, and the increase of the opportunity cost of non-intervention and punishment to the other two parties, the faster the convergence of the central government's choice of intervention strategy. The increase of the rewards and punishments, and the increase of negotiation profits, non-negotiation costs, and loss of reputation for non-negotiation, and the decrease in the negotiation costs and non-negotiation profits all contribute to the shortening of the convergence time of the local government's choice of negotiation strategy. The increase of the rewards and punishments, the increase of support profits, the loss of reputation for non-support, and the decrease of the profits for non-support and the extra cost for support will promote the convergence of China Railway's choice of the support strategy. Finally, the simulation results are discussed so as to target feasible coordination strategies, which provides a theory basis for the practice of conflict coordination among stakeholders of regional high-speed rail project at the investment decision stage.

Asynchronous Life Histories Generate Uneven Arms Races and Impact the Maintenance of Mutualisms.

AbstractMutualisms constitute a diverse class of ecologically important interactions, yet their ecological and evolutionary stability remain topics of debate in coevolutionary theory. Recent theoretical and empirical work has suggested that coevolutionary arms races may be involved in the maintenance of mutualistic interactions, sustaining mutually beneficial outcomes for interacting species while producing exaggerated traits. Here we present an individual-based model that evaluates how asynchronous life histories-that is, partners with different average lifespans-change the dynamics of trait coevolution, the expected fitness outcomes for species involved, and the dynamics of selection differentials across time for each species. Results indicate that a longer-lived mutualist will consistently "lose" an otherwise balanced coevolutionary arms race, being outpaced in both the mean trait value and fitness outcome compared with a shorter-lived partner. Furthermore, linear selection differentials on mutualistic traits become increasingly divergent as life histories become increasingly asynchronous, with the longer-lived species experiencing persistent directional selection and the shorter-lived species experiencing weaker, more inconsistent selection. These results suggest that asynchronous life histories can complicate the maintenance of mutualistic interactions via coevolutionary arms races and that detecting coevolution via selection differentials may be difficult when life histories are sufficiently divergent.

Multi‐agent behavioral strategy game and synergy evolution path of the retired power battery recycling system

AbstractIn the era of transitioning to a low‐carbon economy, China's electric vehicle market is experiencing exponential growth, a trend that is mirrored by the burgeoning market for the recycling of used power batteries. This rapid expansion underscores the urgency of enhancing the power battery recycling system. However, the lack of sufficient engagement from consumers and recyclers in the recycling system currently impedes the formation of a cohesive recycling effort. To tackle this issue, we introduce a tripartite evolutionary game model involving the government, battery recyclers, and consumers. By analyzing the evolutionary stability strategies (ESSs) and their stability conditions, we delineate the synergetic development paths of them. Numerical examples illustrate the model's effectiveness and explore how government incentive policies can affect the strategic decisions of other participants. Our findings suggest that establishing incentive policies by the government in the early stage of the power battery industry is crucial for fostering a synergetic recycling system that is led by the government, involved by the community, and driven by the market.

Learning a complex network representation for shape classification

Shape contour is a key low-level characteristic, making shape description an important aspect in many computer vision problems, with several challenges such as variations in scale, rotation, and noise. In this paper, we introduce an approach for shape analysis and classification from binary images based on representations learned by applying Randomized Neural Networks (RNNs) on feature maps derived from a Complex Network (CN) framework. Our approach models the contour in a complex network and computes their topological measures using a dynamic evolution strategy. This evolution of the CN provides significant information into the physical aspects of the shape’s contour. Therefore, we propose embedding the topological measures computed from the dynamics of the CN evolution into a matrix representation, which we have named the Topological Feature Map (TFM). Then, we employ the RNN to learn representations from the TFM through a sliding window strategy. The proposed representation is formed by the learned weights between the hidden and output layers of the RNN. Our experimental results show performance improvements in shape classification using the proposed method across two generic shape datasets. We also applied our approach to the recognition of plant leaves, achieving high performance in this challenging task. Furthermore, the proposed approach has demonstrated robustness to noise and invariance to transformations in scale and orientation of the shapes.

Evolutionary game analysis on collaborative logistics diffusion considering diminishing marginal utility under dynamic productivity

Reducing carbon emissions in the transportation sector is crucial for sustainable practices, with collaborative logistics diffusion playing a significant role in promoting energy-saving and emission-reduction strategies. This paper introduces an evolutionary game model to analyze collaboration between manufacturers and logistics service providers (LSPs), using the Hotelling quadratic curve model to measure the diminishing marginal utility of basic profits. Initially, a static strategy leads to a cyclic evolution without reaching the evolutionary stable strategy (ESS), reflecting competitive dynamics under static productivity conditions where neither party gains a lasting advantage, leading to periodic adjustments in strategies. The Cobb-Douglas function models a dynamic productivity strategy that regulates the extra benefits of collaborative logistics and penalties for 'free-riding.' This strategy demonstrates an inward spiral convergence towards the ESS, showing that adaptive mechanisms enable manufacturers and LSPs to align interests and achieve equilibrium. Enhanced benefits from collaborative logistics promote a stable and cooperative ecosystem, discouraging free-riding and enhancing cost efficiency. Moreover, increased investment by manufacturers not only strengthens their position but also fosters an environment conducive for LSP participation, expediting collaborative logistics diffusion and minimizing potential losses.

Evolutionary Tripartite Game Model in the Coal Mine Risk-Hazard Double Pre-Control and Management in China

The classification, investigation, and management of the safety risk (namely, risk-hazard double pre-control management mechanism) in coal mines were introduced into a behavioral evolution game to assess the decision-making processes of coal mine workers, regulators, and supervisors. A tripartite game tree and a revenue perception matrix were constructed, and the evolutionary stability strategy (ESS) achieved by the evolutionary system under different conditions was obtained according to the replication dynamic equation (RD). The numerical simulation of the evolution game was conducted in MATLAB, which verified the influence of various parameters on the behaviors adopted by all parties. The results show: (1) When the safety, regulatory, and supervisory costs are high, the average expected value of mine workers, regulators, and supervisors to choose safety behaviors is low, so they adopt an unsafe behavior. By contrast, reducing costs can encourage the adoption of safety behaviors. (2) When the punishment for accident losses caused by unsafe behavior and the lack of regulations and supervision is higher than 50% of the initial state, the actors in the tripartite game are more inclined to avoid risks when facing cost losses in the short term, which can lead them to choose a safety behavior. (3) When the actors in the tripartite game face different accident probabilities and accident loss, with the severity of the accident increasing to 80% of the initial state, mine workers and regulators gradually adopt safety and regulation behaviors, and supervisors quickly adopt supervision methods.

Evolutionary game and simulation analysis of three parties of electricity market regulation under government participation mechanism

Promoting the widespread access and integration of new energy generation into existing power markets is a key strategy for China to achieve peak carbon and carbon neutrality. A joint regulatory mechanism composed of government regulators, power generation enterprises, and third-party testing organizations has become an effective way to ensure the stable operation of the power market and safeguard the economic interest of all parties. In order to analyze the key factors affecting the mechanism and improve the regulatory efficiency, this paper constructs a three-party evolutionary game model, explores the motivations of the participants under different strategy choices, and establishes a set of indicator systems for evaluating producers. The evolutionary game model is numerically simulated using the unified dynamics method, and the evolutionary stable strategy analysis is used to reveal how the key parameters affect the outcome of the game and its dynamic process, so as to find out the potential driving factors affecting the strategy choices. The results of the study show that in the early stage of market development, it is crucial to improve the profitability of firms, and the government needs to impose strict penalties and high incentives. As the market gradually matures and becomes more transparent, the government can gradually reduce the level of penalties. At the same time, ensuring accountability mechanisms from higher authorities to the government is key to ensure the effectiveness of regulation. This study provides an important theoretical basis and policy recommendations for constructing a new regulatory structure for the electricity market.

Reentry trajectory optimization of hypersonic glide vehicle based on improved particle swarm algorithm

The reentry process of hypersonic glide vehicle presents a host of intricate constraint issues. A method for optimizing the reentry trajectory of hypersonic glide vehicle is presented to achieve the shortest possible reentry time. The approach utilizes the improved particle swarm optimization (IPSO) algorithm. Firstly, population initialization is performed using the quasi-oppositional differential evolution (QODE) strategy to promote diversity within the population. Then an adaptive adjustment algorithm is designed for the inertia weight coefficient and learning factor to achieve a balance between global and local search capabilities. Finally, utilizing the IPSO algorithm for optimizing the flight parameters of the reentry trajectory, the reentry time is reduced from 338 seconds to 335 seconds. The simulations results indicate that the IPSO algorithm outperforms both the standard PSO algorithm and genetic algorithm (GA) in terms of optimization performance index.

Aviation fuel pump health state assessment based on evidential reasoning and random forests

AbstractAs the power source of the engine, the Fuel Pump(FP) plays a vital role in the safe operation of the aircraft. Due to the complexity of the working mechanism of Aviation Fuel Pumps (AFP) and the strong correlation between the components, the assessment model cannot reflect the health state of the FPs better, while the initial parameters in the assessment model will affect the assessment effect of the model. Therefore, this paper proposes a health status assessment model that can fully integrate monitoring data. To improve the accuracy of the model parameters, the Random Forest algorithm is used to give the feature weights to make up for the limitation of relying on expert knowledge, and the model parameters are optimized by the Covariance Matrix Adaptive Evolutionary Strategy algorithm, which achieves an accurate assessment of the state. Finally, the AFP test bed was built and the AFP was tested. Compared with other methods, the accuracy of the proposed method in this question reaches 96%, which is greatly superior to other methods and verifies the effectiveness of the proposed method. It also provides an outlook on future research directions for health state assessment.

The speed of neutral evolution on graphs.

The speed of evolution on structured populations is crucial for biological and social systems. The likelihood of invasion is key for evolutionary stability. But it makes little sense if it takes long. It is far from known what population structure slows down evolution. We investigate the absorption time of a single neutral mutant for all the 112 non-isomorphic undirected graphs of size 6. We find that about three-quarters of the graphs have an absorption time close to that of the complete graph, less than one-third are accelerators, and more than two-thirds are decelerators. Surprisingly, determining whether a graph has a long absorption time is too complicated to be captured by the joint degree distribution. Via the largest sojourn time, we find that echo-chamber-like graphs, which consist of two homogeneous graphs connected by few sparse links, are likely to slow down absorption. These results are robust for large graphs, mutation patterns as well as evolutionary processes. This work serves as a benchmark for timing evolution with complex interactions, and fosters the understanding of polarization in opinion formation.

Carbon tax-driven technological innovation may accelerate the directional recovery of waste cooking oil into bio-jet fuel: An evolutionary game approach

Biofuel production from waste cooking oil (WCO) offers an alternative to fossil fuels, especially for high-value bio-jet fuel. However, this industry is hindered by informal recyclers who covertly divert large amounts of WCO to illegal gutter oil production. Investigating the dynamic evolution of stakeholder behavior will help explore solutions. Thus, this study presents a tripartite evolutionary game model that includes the government, formal recyclers, and informal recyclers, aims to redesign the government intervention strategy to promote the directional flow of WCO from restaurant trash cans to bio-jet fuel production. We find that the evolutionary game model exists eight possible evolutionary stability strategies (ESSs), and the choice of each ESS depends mainly on the trade-off between costs and revenues for each stakeholder. The numerical study results reveal that formal recyclers are driven to carry out technological innovation by government support, profiting from bio-aviation kerosene products, and income from carbon emission reduction. These factors also have an indirect impact on the transformation of informal recyclers. Therefore, the government should provide adequate support for technological innovation to formal recyclers and increase their profitability of products to enable them to actively implement innovative strategies. This can be achieved by expanding the sales channels of bio-jet fuel products, implementing patent protection measures, and improving the carbon reduction trading mechanism. Furthermore, the government's high tax rate on formal recyclers and the significant profits earned by informal recyclers through illegal gutter oil production may dissuade them from transitioning their businesses. Above findings are in line with the actual issues of WCO recycling and provide a new dynamic decision-making method for enterprises and government managers.

Dual stress factors adaptive evolution for high EPA production in Schizochytrium sp. and metabolomics mechanism analysis.

Eicosapentaenoic acid (EPA) is a vital ɷ-3 polyunsaturated fatty acid (PUFA) for human body with various physiological functions. In this study, we proposed an adaptive evolutionary strategy based on high-temperature and high-oxygen two-factor stress to increase the EPA production capacity of Schizochytrium. High-temperature stress was used to increase EPA yield, and high oxygen was implemented to continuously stimulate cell growth and lipid accumulation. The biomass and EPA production of ALE-D50 reached 35.33g/L and 1.54g/L, which were 43.85% and 71.11% higher than that of the original strain, respectively. Lower in vivo reactive oxygen species levels indicated that the evolved strain possessed stronger antioxidant activity. Liquid chromatography-mass spectrometry metabolomics showed that enhanced glucose consumption and glycolysis metabolism, as well as a weakened tricarboxylic acid cycle and reduced amino acid metabolic tributaries in the evolved strain, might be associated with increased growth and EPA synthesis. Finally, the lipid production and EPA production in a fed-batch fermentation were further increased to 48.93g/L and 3.55g/L, improving by 54.30% and 90.86%, respectively. This study provides a novel pathway for promoting EPA biosynthesis in Schizochytrium.

A pareto fronts relationship identification-based two-stage constrained evolutionary algorithm

Striking a balance between diverse constraints and conflicting objectives is one of the most crucial issues in solving constrained multi-objective optimization problems (CMOPs). However, it remains challenging to existing methods, due to the reduced search space caused by the constraints. For this issue, this paper proposes a Pareto fronts relationship identification-based two-stage constrained evolutionary algorithm called RITEA, which balances objective optimization and constraint satisfaction by identifying and utilizing the relationship between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF). Specifically, the evolutionary process is divided into two collaborative stages: training stage and reinforcement stage. In the training stage, a relationship identification method is developed to estimate the relationship between UPF and CPF, which guides the population search direction. In the reinforcement stage, the corresponding evolutionary strategies are designed based on the identified relationship to enhance the accurate search on the CPF. Furthermore, a dynamic preference fitness function (termed DPF) is designed to adaptively maintain the balance of search preference between convergence and diversity. Compared to seven state-of-the-art algorithms on 36 benchmark CMOPs in three popular test suites, RITEA obtains 77.8% of the best IGD values and 66.7% of the best HV values. The experimental results show that RITEA exhibits highly competitively when dealing with CMOPs.

Which policy can effectively promote the formal recycling of power batteries in China?

China's power battery recycling (PBR) market is embryonic, facing a 'Gresham's Law’. A critical concern for the government is ascertaining policies that can effectively enhance formal PBR and boost the competitiveness of authorized channels. Our study develops an evolutionary game model incorporating the government, recyclers, and consumers, investigating the impact of subsidies for formal recyclers, consumer subsidies, and deposit refund policies on the formal recycling process. The main conclusions are as follows. (1) The evolutionary strategy for the PBR game system is (non-regulation, formal recycling, green behavior) when recyclers acquire substantial reputation gains from formal recycling, and a non-regulation approach outstrips the government's benefits from regulation. (2) Subsidies for recyclers are more effective in fostering formal recycling of power batteries than consumer subsidies. When the government subsidy to formal recyclers increases from 0 yuan/kWh to 40 yuan/kWh, The evolutionary stable state transitions from (regulation, informal recycling, non-green behavior) to (non-regulation, formal recycling, green behavior). The incentive impact of consumer subsidies is relatively negligible. (3) The deposit refund policy significantly advances formal PBR, with both the increase in deposit amounts and refund ratios capable of reversing the low rates of formal recycling.

Microbial-mediated conversion of soil organic carbon co-regulates the evolution of antibiotic resistance

The influence of organic carbon on the proliferation of antibiotic resistance genes (ARGs) in the soil has been widely documented. However, it is unclear how soil organic carbon (SOC) interacts with the evolution of antibiotic resistance in bacteria. Here, we examined the variations in ARGs abundance during SOC mineralization and explored the microbiological mechanisms and key metabolic pathways involved in their coevolution. The results showed that the SOC mineralization rate was closely correlated with ARGs abundance (p < 0.05). High organic carbon (OC) mineralization was conducive to the occurrence of multidrug resistance genes. For example, multidrug_transporter and mexB increased 2.26 and 7.83 times from the initial level. The competitor (stress) evolutionary strategy model revealed that higher OC inputs drive environmental microorganisms to evolve from stress tolerant to high resistance and strong adaptation. Meta-genomic and transcriptomic analyses revealed that the conversion process of pyruvate to acetyl-CoA to acetate was the critical metabolic pathway for the co-regulation of antibiotic resistance. Gene deletion validation trials have demonstrated that the key functional genes (ackA and pta) involved in this process can modulate the development of vancomycin and multidrug resistance. This outcome provides a preliminary framework for microbial mechanisms that target the co-regulation of microbial OC conversion and the evolution of antibiotic resistance.

Research on evolutionary game of digital twin data information sharing based on blockchain technology

Due to the significant characteristics of decentralization and tamper resistance of blockchain technology, it helps to solve the problems of timeliness and slow permission control in traditional information sharing. However, most researchers are committed to exploring how to improve the efficiency, reliability, and privacy security of digital twin data. There is a lack of research on issues such as blockchain participants free-riding and not actively participating in information sharing, and how to promote information sharing through incentives. Therefore, this paper designs a distance grouping based practical byzantine fault tolerance consensus algorithm to address the credibility issue of transmitted data, thereby improving the efficiency of negotiation. The feasibility of the algorithm is verified through cases analysis. In addition, for the problem of low subjective willingness of information sharing, this paper designs an information sharing incentive strategy based on blockchain intelligent contracts, establishes digital twin data sharing evolutionary game models with reputation incentives and non-reputation incentives, so as to explore the impact of reputation incentive on evolutionary stability strategies. Finally, simulation analysis is conducted on the evolutionary game model of digital twin data sharing. The impact of several key indicators such as data complementarity, trust, positive incentive coefficient, and data sharing cost on the evolution process of information sharing are considered, and corresponding management insights are obtained.

Adult Human, but Not Rodent, Spermatogonial Stem Cells Retain States with a Foetal-like Signature.

Spermatogenesis involves a complex process of cellular differentiation maintained by spermatogonial stem cells (SSCs). Being critical to male reproduction, it is generally assumed that spermatogenesis starts and ends in equivalent transcriptional states in related species. Based on single-cell gene expression profiling, it has been proposed that undifferentiated human spermatogonia can be subclassified into four heterogenous subtypes, termed states 0, 0A, 0B, and 1. To increase the resolution of the undifferentiated compartment and trace the origin of the spermatogenic trajectory, we re-analysed the single-cell (sc) RNA-sequencing libraries of 34 post-pubescent human testes to generate an integrated atlas of germ cell differentiation. We then used this atlas to perform comparative analyses of the putative SSC transcriptome both across human development (using 28 foetal and pre-pubertal scRNA-seq libraries) and across species (including data from sheep, pig, buffalo, rhesus and cynomolgus macaque, rat, and mouse). Alongside its detailed characterisation, we show that the transcriptional heterogeneity of the undifferentiated spermatogonial cell compartment varies not only between species but across development. Our findings associate 'state 0B' with a suppressive transcriptomic programme that, in adult humans, acts to functionally oppose proliferation and maintain cells in a ready-to-react state. Consistent with this conclusion, we show that human foetal germ cells-which are mitotically arrested-can be characterised solely as state 0B. While germ cells with a state 0B signature are also present in foetal mice (and are likely conserved at this stage throughout mammals), they are not maintained into adulthood. We conjecture that in rodents, the foetal-like state 0B differentiates at birth into the renewing SSC population, whereas in humans it is maintained as a reserve population, supporting testicular homeostasis over a longer reproductive lifespan while reducing mutagenic load. Together, these results suggest that SSCs adopt differing evolutionary strategies across species to ensure fertility and genome integrity over vastly differing life histories and reproductive timeframes.