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The equipment manufacturing industry, as a strategic industry of China, is experiencing a transition from imitative innovation to independent innovation. The achievements of independent innovation have not been as good as could have been expected. Based on evolutionary economics, the purpose of this paper is to explore the evolutionary path of the two innovation modes, respectively, and analyze the internal and external factors that hinder the mutation from imitative innovation routine to independent innovation routine. According to the results of the evolutionary game model, several policy suggestions are proposed to promote the transition from imitative innovation to independent innovation.

This paper is based on the concepts of evolutionary economics. Routine, mutation, path dependence and selection are included in the analysis of the evolutionary path of the two innovation modes. Especially, the evolutionary game model of innovation modes selection is established to explain how internal and external conditions work in the transition.

The paper explores the evolutionary path of the transition from imitative innovation to independent innovation in the equipment manufacturing industry of China, and analyses the obstacles and factors (internal path dependence, and the lack of benefit incentive and external mutation conditions such as fiscal support and intellectual property protection) that hinder the mutation from imitative innovation routine to independent innovation routine. The results of the evolutionary game model show that the pursuit of the benefit (innovation return or the profit), as an internal mutation condition, is the most fundamental motivation for independent innovation, while policy incentives, as the external mutation conditions, have a significant impact on the evolutionary transition. According to the results, several policy suggestions are proposed to promote the transition from imitative innovation to independent innovation.

Taking the equipment manufacturing industry as a particular object, this paper tries to explain the evolutionary path and the obstacle factors of the transition from imitative innovation to independent innovation from the perspective of evolutionary economics, involving routine, mutation, path dependence, selection, and so on. The evolutionary game model of innovation modes selection is established to investigate the influence of these factors.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.

This study advocates the importance of taking an evolutionary perspective in the strategic configuration of closed-loop supply chains (CLSC) in the transition to a circular economy. Building on the supply chain management and industrial dynamics research domains, an evolutionary analytical framework was developed and applied in the empirical context of the ongoing industrial transition to e-mobility.

This study is designed as an in-depth exploratory case study to capture the multi-layer dynamic complexities and their interplay in CSLC development. The empirical investigation was based on two-year interactions between the authors and various departments in a leading European heavy vehicle manufacturer. The proposed evolutionary analytical framework was used for investigating the dynamics of four CLSC configurations through ten possible trajectories.

The findings demonstrate that the evolution of each CLSC configuration comes with multiple challenges and requirements and point out the necessity for the co-development of technologies, product design and production, and infrastructure through long-term relationships among key supply chain actors. However, this evolutionary journey is associated with multiple dilemmas caused by uncertainties in the market and technology developments. All these factors were properly captured and critically analyzed, along with their interactions, thanks to the constructs included in the proposed evolutionary analytical framework.

The proposed evolutionary framework is applicable for examination of SC transformation in the context of market and technology development, and is particularly relevant for transitioning from linear SC to CLSC. The framework offers a single actor perspective, as it does not directly tackle dynamics and effects of actions taken by SC actors.

The developed framework can support SC managers in identifying, framing, and comparing alternative strategies for CLSC configuration in the transition process.

This study proposes the framework for understanding and guiding the evolutionary process of CLSC development. Its uniqueness lies in the integration of concepts from innovation and evolutionary theories coming from industrial dynamics and SCM literature streams.

This paper aims to propose the mechanism of cross-network effect embedded, which can help cross-border e-commerce (CBEC) platforms strengthen cooperative relationships with sellers more equitably and effectively by using the network structural characteristics of the platforms themselves.

A two-stage evolutionary game model has been used to confirm the influence factors. The mathematical derivation of evolutionary game analysis is combined with the simulation method to examine the role of cross-network effect in cooperation. The evolutionary game model based on the cross-network effect is proposed to achieve better adaptability to the study of cooperation strategy from the two-sided market perspective.

The evolutionary game model captures the interactions of cross-network effect and the influence factors from a dynamic perspective. The cross-network effect has a certain substitution on the revenue-sharing rate of SMEs. CBEC platforms can enhance the connection between consumers and the website by improving the level of construction, which is a good way to attract sellers more cost-effectively and efficiently.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specificCBEC platforms.

This study provides a new method for the validation of the cross-network effect, especially when data collection is difficult. But this method is only a numerical simulation. So the conclusions still need to be further tested empirically. Besides, researchers are advised to explore the relationship between the added user scale and the cross-network effect in some specific CBEC platforms.

Investigations that study cooperation strategy from the cross-network effect perspective in CBEC are limited. The research figured out which influence factors are affected by the cross-network effect in cooperation. A two-stage evolutionary game model was proposed to explain the interaction of the factors. The evolutionary game analysis with a simulation method was combined to highlight the role of cross-network effect on cooperation strategy to give a deeper investigation into the sustainable cooperation ofCBEC.

Although green logistics has become a new focus of cooperation between government and enterprises under environmental constraints, how local governments formulate subsidy policies to effectively guide the green transformation of regional logistics and how to facilitate the reasonable cost-sharing are rather critical. This paper will deeply explore the dynamic process of the tripartite participation (government, platform, and logistics enterprises) in the selection of regional green logistics strategy, and reveal the evolutionary game relationship of the three parties.

To explore the dynamics involving the government, platform and logistics enterprises for the green logistic transformation, and reveal the evolutionary gaming among the three parties, based on the bounded rationality premise, this study constructs the tripartite asymmetric evolutionary game models, uses the stability theorem of differential equation to explore the evolution and stability strategy of the system in different cases and explicates the paths of influence on the tripartite behaviors via simulations.

Results of this study indicate that there exist stable equilibrium strategies among the three parties regarding the regional green logistics, and they are affected by different factors. The government's subsidy, subsidy intensity and the platform's cost-sharing proportion can generate positive effects, but the latter two can also impact negatively beyond the effective ranges. The findings provide a theoretical basis for local governments, platforms and logistics enterprises to formulate justifiable subsidy intensity and determine reasonable sharing proportion.

Firstly, considering the significant relevance of local government, it is included in the evolution model, and the tripartite game (among government, platform and enterprises) is explored; Secondly, by comparing the equilibrium results under different game conditions, this paper analyzes the evolution of each party's game strategy to achieve the optimal return under bounded rationality and the important factors determining the strategic selection; Finally, the key factor of platform cost sharing is involved, and to what extent the change of platform cost sharing ratio will influence the systematic stability is explored.

The purpose of this paper is to demonstrate the applicability of swarm and evolutionary techniques for regularized machine learning. Generally, by defining a proper penalty function, regularization laws are embedded into the structure of common least square solutions to increase the numerical stability, sparsity, accuracy and robustness of regression weights. Several regularization techniques have been proposed so far which have their own advantages and disadvantages. Several efforts have been made to find fast and accurate deterministic solvers to handle those regularization techniques. However, the proposed numerical and deterministic approaches need certain knowledge of mathematical programming, and also do not guarantee the global optimality of the obtained solution. In this research, the authors propose the use of constraint swarm and evolutionary techniques to cope with demanding requirements of regularized extreme learning machine (ELM).

To implement the required tools for comparative numerical study, three steps are taken. The considered algorithms contain both classical and swarm and evolutionary approaches. For the classical regularization techniques, Lasso regularization, Tikhonov regularization, cascade Lasso-Tikhonov regularization, and elastic net are considered. For swarm and evolutionary-based regularization, an efficient constraint handling technique known as self-adaptive penalty function constraint handling is considered, and its algorithmic structure is modified so that it can efficiently perform the regularized learning. Several well-known metaheuristics are considered to check the generalization capability of the proposed scheme. To test the efficacy of the proposed constraint evolutionary-based regularization technique, a wide range of regression problems are used. Besides, the proposed framework is applied to a real-life identification problem, i.e. identifying the dominant factors affecting the hydrocarbon emissions of an automotive engine, for further assurance on the performance of the proposed scheme.

Through extensive numerical study, it is observed that the proposed scheme can be easily used for regularized machine learning. It is indicated that by defining a proper objective function and considering an appropriate penalty function, near global optimum values of regressors can be easily obtained. The results attest the high potentials of swarm and evolutionary techniques for fast, accurate and robust regularized machine learning.

The originality of the research paper lies behind the use of a novel constraint metaheuristic computing scheme which can be used for effective regularized optimally pruned extreme learning machine (OP-ELM). The self-adaption of the proposed method alleviates the user from the knowledge of the underlying system, and also increases the degree of the automation of OP-ELM. Besides, by using different types of metaheuristics, it is demonstrated that the proposed methodology is a general flexible scheme, and can be combined with different types of swarm and evolutionary-based optimization techniques to form a regularized machine learning approach.

With the growing demand for infrastructure and public services in recent years, PPP-UP have attracted a great deal of attention. However, while the user focuses on the payment for use and the private sector is concerned with its return on investment, the public sector pays more attention to the efficient utilization of public funds. In order to analyze the willingness of each stakeholder to join PPP-UP, an evolutionary game model involving the three parties is constructed.

An evolutionary game model is established that considers the users and the public and private sectors in user-pay public-private-partnership projects (PPP-UP). Eight scenarios of equilibriums and the game's evolutionary stable strategies are analyzed, and the corresponding stability conditions are then obtained. A situation where all three players are willing to cooperate in theory is also examined. The key influencing parameters that affect cooperation behaviors are further discussed.

First, the results illustrate that by properly adjusting the influencing factors, the cooperation status among the three parties can be changed along with certain evolutionary trends. Second, it is hard to modify unsatisfactory evolutionary stability by small changes in both the price compensation of and the construction and operation compensation. Third, it is necessary to involve the users in the decision-making process in PPP-UP and take their demands regarding benefits and payments into account.

In this paper, we focus on PPP-UP to research interactions among the public and private sectors and the users. Based on the analysis of the evolutionary game, to facilitate the successful implementation and development of a project, several conditions are needed to ensure tripartite cooperation. Several recommendations are then proposed for decision-makers in PPP-UP.

The purpose of this study is to fill a gap in evolutionary theorizing in the field of information systems. Evolutionary theorizing has recently been added as a useful tool to the research repertoire of information systems investigators. However, the literature on evolutionary theorizing and related empirical research lacks a clear framework that explicitly shows how information systems researchers can go, step-by-step, from a generic model of the evolution of traits in our ancestral past to a more specific model depicting the effects of technology facilitation of those traits among modern humans. The purpose of this study is to fill this gap through a framework composed of six stages.

To discuss and illustrate the framework, the authors develop an easy-to-understand generic path model explicitly depicting relationships among variables related to events that occurred in our evolutionary past. We then incrementally adapt this generic path model, eventually arriving at a focused path model depicting causal relationships among social networking site use, job satisfaction, organizational commitment and job performance. In doing so, the authors also develop a theoretical model about how social networking site use can affect job performance, where a positive total effect is predicted via positive intermediate effects on job satisfaction and organizational commitment.

To discuss the final stage in the framework, the authors present an illustrative example where the focused path model is tested based on a study of the effect of Facebook use on job performance among 178 working professionals across the USA. This illustrative example provides general support for the theoretical model.

The counterintuitive hypothesis that Facebook use is associated with increased job performance is supported.

Social networking site use by organizational employees is likely to be associated with improved job performance.

This study provides a clear framework that shows how researchers can go from a generic evolutionary path model in our ancestral past to a more specific model comprising technology effects in modern humans.

Because belief in a supernatural agent with extraordinary power is rooted in psychology, Veblen's instinct psychology was the essential basis for his evolutionary economics of religion. The innate behavioral traits that Veblen called instincts in human nature are now recognized in evolutionary psychology as domain-specific mechanism that evolved as adaptations to enable human survival and reproduction. The authors aim to explain how the modern evolutionary psychology of religion provides a modern psychological basis for Veblen's evolutionary economics of religion.

First, the authors review how Veblen's theory of an evolved human nature of instincts was applied to explain the origins of religion in primitive societies and remained a resilient force despite evolutionary erosion of institutional religion as science advanced. Second, the authors note how evolutionary psychology explains the origins of religion in terms of the functioning of domain-specific psychological mechanisms that evolved as adaptations for purposes other than religion.

The similarities between Veblen's instinct psychology and the explanation of religion as by-products of domain-specific psychological mechanisms are sufficient to allow the conclusion that the evolutionary psychology of religion provides a modern psychological basis for Veblen's evolutionary economics of religion.

An evolutionary economics of religion has a great social value if it provides credible explanations of both the origins of religious belief and innate tendency for religious belief to continue even as science refutes elements of religious doctrines. With a modern psychological basis, Veblen's evolutionary economics of religion accomplishes that purpose.

This qualitative study attempts to understand what kinds of evolutionary selection and variation occur in family businesses during the preparation of a managerial and ownership succession.

The study was conducted by interviewing members of one family business in Louisiana, USA and one in Finland in order to contribute to the understanding of succession preparation in small family businesses with two generations. Evolutionary economics was adapted for this interdisciplinary study to explain evolutionary changes in a family business succession.

The findings indicate that both selection and variation can take place through different routes during the preparatory phase of a family business succession. Selection is influenced both by the founder and next generations. However, it does not occur in company A due to the reluctance of the younger generation. In company B selection is processed through joint thinking and visioning. This will lead to variation which is shaped by both generations.

This study is based on qualitative interpretation. Limitations of the study are the small number of informants and the lack of generalization of the results.

This study shows that selection and variation are intertwined. If selection does not occur in a family business, it leads to no variation between the generations. However, exits are possible; death and birth of companies are part of the life cycle of family businesses.

Evolutionary thinking has not been studied recently among family firms except in the field of evolutionary psychology. Evolutionary thinking offers a variety of topics to study in the future.