Co-evolutionary Approach Research Articles

Rethinking chaîne opératoire beyond cognitivist approaches

AbstractThis article proposes a reconsidered chaîne opératoire framework drawing on theories of embodied, extended, and enacted cognition. I investigate three hypotheses: (1) the chaîne opératoire framework (as it is used) takes a cognitivist approach to the mind, (2) technical tendencies and milieus can encompass and support modern theories of embodied, extended, enactive cognition, and (3) that by reconsidering these elements of the chaîne opératoire framework alongside contemporary theories of cognition we may re-envision a novel chaîne opératoire framework which takes a non-cognitivist approach. First, the development of chaîne opératoire and its uses are reviewed and analyzed for evidence of a cognitivist approach. Second, I argue that elements of the chaîne opératoire framework (technical tendencies and milieus) can and do support contemporary theories of cognition. Similar methods for connecting methodological frameworks with the phenomenological nature of human being such as Cognigrams and the Four-Field Co-Evolutionary approach are analyzed to provide context for this work. Following this, I outline a unique approach to the chaîne opératoire framework which accounts for theories of embodied, enacted, and extended cognition.

A co-evolutionary approach to MNE-assisted industry development: Uruguay’s forest-based pulp sector

Purpose This study aims to investigate how co-evolutionary interactions between multinational enterprises (MNEs) and local actors shaped resource-intensive industry development over several decades, examining the economic, industrial and social implications of these interactions. It details the long-term effects of MNEs’ activities on the host country’s economic and social development and unfolds the path-dependent sequence and mechanisms of occurrence. Design/methodology/approach In a historical case study of Uruguay’s forest-based pulp sector during the 1970s to 2023, the authors analyze data from interviews with local industry actors and from company archives, academic publications and public environmental organizations. Findings The findings untangle the path-dependent co-evolution between MNEs’ activities and local actors and the resulting gradual development of the local industry. The increasing commitment of MNEs to the host country and their engagement with business and social communities impact the development of the industry’s technology basis, activity networks, identity and market. Spillovers and linkages occur in these interactions, driving industry development processes forward. The authors also reveal how MNEs attempt to address the social and environmental tensions associated with their operations to support their long-term presence in the host country. Research limitations/implications The authors contribute to the MNE-assisted development literature by untangling the specific mechanisms of MNEs’ engagement in local industry development over a long-term period and elaborating on its industrial and social implications. The authors enhance knowledge about spillovers by capturing a wide range of spillover and linkage effects and assessing their long-term impacts on industry development. Practical implications The authors offer insights into how policymakers can tailor instruments for attracting MNEs by adopting a long-term perspective regarding the implications for local development and accounting for economic, industrial and social impacts. This approach can maximize beneficial outcomes from MNEs’ presence and limit tensions between MNEs and local actors. Social implications This historical case study illustrates the complexity of MNEs’ engagement with local actors over time, including various tensions and positive developments. It illustrates the importance of social acceptance in achieving quality linkages between local and foreign actors. Originality/value This historical analysis untangles the path dependency in the long-term impacts of MNE-assisted development, illustrating the value of a temporal perspective.

Cause and effect in biology, culture, and the (extended) mind: a coevolutionary approach

Cause and effect in biology, culture, and the (extended) mind: a coevolutionary approach

A comparative study of evolutionary algorithms and particle swarm optimization approaches for constrained multi-objective optimization problems

Many real-world optimization problems contain multiple conflicting objectives as well as additional problem constraints. These problems are referred to as constrained multi-objective optimization problems (CMOPs). Many meta-heuristics for solving CMOPs, called constrained multi-objective meta-heuristics (CMOMHs) have been introduced in the literature, including those using particle swarm optimization (PSO)(Kennedy and Eberhart, 1995), genetic algorithms (GAs)(Man et al., 1996), and differential evolution (DE)(Storn and Price, 1997). CMOMHs can be grouped into four different classes: classic CMOMHs, co-evolutionary approaches, multi-stage approaches, and multi-tasking approaches. An extensive comparative study of twenty different CMOMHs on a wide variety of test problems, including real-world CMOPs in the fields of science and engineering, is conducted. A multi-swarm PSO approach called constrained multi-guide particle swarm optimization (ConMGPSO) is introduced and compared to the best-performing previous approaches according to the comparative study. The performance of each algorithm was found to be problem dependent, however the best overall approaches were ConMGPSO, paired-offspring constrained evolutionary algorithm (POCEA)(He et al., 2021), adaptive non-dominated sorting genetic algorithm III (A-NSGA-III)(Jain and Deb, 2014), and constrained multi-objective framework using Q-learning and evolutionary multi-tasking (CMOQLMT)(Ming and Gong, 2023). ConMGPSO and POCEA had the best performance on the CF benchmark set, which contains examples of bi-objective and tri-objective CMOPs with disconnected CPOFs. The CMOQLMT approach had the best performance on the DAS-CMOP benchmark set, which contain additional difficulty in terms of feasibility-, convergence-, and diversity-hardness. For the selected real-world CMOPs, A-NSGA-III had the best performance overall. ConMGPSO was shown to have the best performance on the process, design, and synthesis problems, and had competitive performance for the power system optimization problems.

Novel Insights into Enzymatic Thermostability: The "Short Board" Theory and Zero-Shot Hamiltonian Model.

Understanding the mechanism underlying thermostabilization in naturally stable enzymes and enhancing the thermostability of unstable enzymes are crucial aspects in enzyme engineering. Despite the development of various engineering methods, there remains substantial scope for improvement. In this study, a novel concept termed as the "short board" theory is proposed, which conceptualizes proteins as barrels with each component representing a jagged board. Notably, optimizing modifications to the shortest board yields optimal enhancements in terms of thermostability performance. To validate this theory, α-amylase, an industrial bulk enzyme with multiple domains, is employed as a model enzyme. The existence of "short boards" and their impact on thermostability modification are demonstrated at the domain, residue, and atomic levels through experimental confirmation using domain substitution. Furthermore, a novel thermostable design and prediction model called Zero-Shot Hamiltonian (ZSH) is established and evaluated on α-amylase. This coevolutionary approach based on thermostability and deep learning exhibits remarkable success exclusively when applied to enzymes with fixed short boards. The integration of the "short board" theory with the ZSH model presents an innovative tool for enhancing enzymatic thermostability.

A coevolutionary approach to institutional lock-in

A coevolutionary approach to institutional lock-in

Two-stage knowledge-assisted coevolutionary NSGA-II for bi-objective path planning of multiple unmanned aerial vehicles

This paper focuses on the bi-objective path planning problem of multiple unmanned aerial vehicles (UAVs) under the complex environment with numerous obstacles and threat areas, where the UAVs need to be kept as far away as possible from threat areas during flight. Based on the integrated energy reduction perspective, a bi-objective model is subtly constructed by minimizing the total energy consumption of each path (including flight altitude, horizontal turns, and path length), and minimizing the costs of the total threats (including ground radar, anti-aircraft gun, missile and geological hazard threat areas). Moreover, a two-stage knowledge-assisted coevolutionary NSGA-II algorithm is novelly proposed to enhance collaboration and avoid collision. The first stage is designed for population convergence, where the considered constrained problem is solved with the help of the designed problem without the constraints of threats and obstacles. The second stage emphasizes the quality and diversity of solutions. In this stage, a double-population coevolution approach is developed. Additionally, a multi-mode strategy is introduced for the inferior population, leveraging reinforcement learning. This strategy aids in selecting the optimal mode from random swing, directed guidance, and potential dominance exploration. Furthermore, experimental results in two different environments show that the proposed algorithm can better solve the collaborative path planning problem for multiple UAVs compared with other five classical or recent proposed algorithms.

Transient social–ecological dynamics reveal signals of decoupling in a highly disturbed Anthropocene landscape

Understanding the transient dynamics of interlinked social-ecological systems (SES) is imperative for assessing sustainability in the Anthropocene. However, how to identify critical transitions in real-world SES remains a formidable challenge. In this study, we present an evolutionary framework to characterize these dynamics over an extended historical timeline. Our approach leverages multidecadal rates of change in socioeconomic data, paleoenvironmental, and cutting-edge sedimentary ancient DNA records from China's Yangtze River Delta, one of the most densely populated and intensively modified landscapes on Earth. Our analysis reveals two significant social-ecological transitions characterized by contrasting interactions and feedback spanning several centuries. Initially, the regional SES exhibited a loosely connected and ecologically sustainable regime. Nevertheless, starting in the 1950s, an increasingly interconnected regime emerged, ultimately resulting in the crossing of tipping points and an unprecedented acceleration in soil erosion, water eutrophication, and ecosystem degradation. Remarkably, the second transition occurring around the 2000s, featured a notable decoupling of socioeconomic development from ecoenvironmental degradation. This decoupling phenomenon signifies a more desirable reconfiguration of the regional SES, furnishing essential insights not only for the Yangtze River Basin but also for regions worldwide grappling with similar sustainability challenges. Our extensive multidecadal empirical investigation underscores the value of coevolutionary approaches in understanding and addressing social-ecological system dynamics.

Cooperative Guidance of Multiple Missiles: A Hybrid Coevolutionary Approach

Cooperative Guidance of Multiple Missiles: A Hybrid Coevolutionary Approach

Социальные практики экологического сознания: региональный аспект

The article analyzes the environmental consciousness of the region's population as a special property of reflecting the social nature of a person, expressed through personal concern about the state of the environment, through the acquisition of environmental information and practical activities to preserve and restore the natural environment. Particular attention in the article is given to theoretical approaches to the consideration of the phenomenon of ecological consciousness. Human consciousness as a "specific property of organized complexity" is considered in the works of Teilhard de Chardin, V. I. Vernadsky, I. R. Prigogine. In the philosophical direction (R. S. Karpinskaya, I. K. Liseev, N. N. Moiseev), a co-evolutionary approach is used, which reflects the unity of human, society and the nature that undergoes changes. The study of environmental consciousness with the help of a rational sociological approach involves the use of statistical methods for researching a particular region. Understanding the ecological consciousness as the three-element system "man – society – nature" enables us to compose a model of ecological consciousness in the form of three components: world perception, information, activity. In the course of the author's sociological study, it was found that a significant number of the of the Saratov region residents (88%) are personally concerned about environmental problems. At the same time, the population is informed both about global environmental problems and about the environmental situation in the region. However, residents of the region towns do not consider themselves sufficiently informed about the environmental problems of their place of residence. Most of the respondents (71%) take an active part in the implementation of environmental activities, for example, 67.5% participated in community work days to clean up the territory; 22.8% – participated in tree planting campaigns; 49.1% try not to use plastic bags and bottles, if possible, etc. The conclusion made is that the environmental consciousness of the population in the Saratov region fully corresponds to the compiled model, due to the fact that the population feels personal responsibility for the state of the environment, is aware of the environmental principles that enable conscious environmental activities of the nature protection to be performed.

Entrepreneurship for sustainable development: co-evolutionary evidence from the tourism sector

PurposeThis paper aims to enhance understanding of how sustainable entrepreneurship (SE) contributes to sustainable development in the tourism sector. To do so, specific factors that act as enablers or inhibitors of SE are identified according to a co-evolutionary lens.Design/methodology/approachA co-evolutionary explanation of the firm? Environment relationship is adopted to undertake a qualitative empirical study of the Castelli Romani tourism destination (Italy), via 23 semi-structured interviews according to a narrative approach.FindingsThe paper demonstrates that entrepreneurs play a crucial role in sustainable development but cannot act in isolation. In fact, according to the co-evolutionary approach, they influence and are influenced by 20 factors. Accordingly, SE can be conceptualised as resulting from effective co-evolutionary interactions between micro (i.e. entrepreneurs and their firm), meso (i.e. the destination where tourism firms are based) and macro (i.e. the wider socio-economic and natural system) levels.Practical implicationsSeveral actions are suggested to entrepreneurs and policymakers to help achieve specific sustainable development goals. These actions focus on: (1) training courses, (2) investments in technologies, (3) creation of innovative business models, (4) exploitation of cultural and natural resources, (5) community involvement and (6) multi-level partnerships.Originality/valueThis is the first study that adopts a co-evolutionary lens to investigate the influencing factors of SE in tourism, shedding light on the effects of their dynamic interdependence. Thus, it provides a more nuanced SE conceptualisation that takes a holistic and dynamic view of sustainability.

Coevolution of Flood Dynamics and Economical Production in Tropical Wetlands: Insights From Bayesian Networks in Ayapel Wetland, Colombia

AbstractThe coevolution approach in socio‐hydrological systems (SHS) allows the analysis of the interactions between social and hydrological systems over time. SHS's characteristics such as non‐linearity, causality, integration of spatiotemporal scales and feedbacks must be considered. However, in simulation processes, there are challenges in analyzing these characteristics while integrating quantitative and qualitative source variables. We propose a general causal network for study of coevolution in tropical wetlands, operationalized through Bayesian Networks for a case study in Ayapel Wetland, Colombia. We defined the flood pulse and economic production to analyze the co‐evolution of tropical wetlands. We identified that the probability of having more than 146,998 individuals of livestock is 30% and of obtaining a fish production considered “high” by fishers is 44.2% for a period from 1995 to 2019. However, under permanent conditions, the probability of this livestock decreases to 18.7%, and “high” fishing increases to 87.5%. Under seasonal conditions, the probability of high livestock production increases to 42.8%, and fish production decreases to 3.85%. We demonstrated how the loss of functionality of the existing hydraulic technology in the study area modifies the production dynamics and how a maximum level of agricultural and livestock production is associated with an increase in forest and grassland cover. We consider that the application of the network strengthens adaptation strategies for climate change or The El Niño‐Southern Oscillation events, the risk management plans and land use decision plans in the tropical wetlands.

Decision variable contribution based adaptive mechanism for evolutionary multi-objective cloud workflow scheduling

Workflow scheduling is vital to simultaneously minimize execution cost and makespan for cloud platforms since data dependencies among large-scale workflow tasks and cloud workflow scheduling problem involve large-scale interactive decision variables. So far, the cooperative coevolution approach poses competitive superiority in resolving large-scale problems by transforming the original problems into a series of small-scale subproblems. However, the static transformation mechanisms cannot separate interactive decision variables, whereas the random transformation mechanisms encounter low efficiency. To tackle these issues, this paper suggests a decision-variable-contribution-based adaptive evolutionary cloud workflow scheduling approach (VCAES for short). To be specific, the VCAES includes a new estimation method to quantify the contribution of each decision variable to the population advancement in terms of both convergence and diversity, and dynamically classifies the decision variables according to their contributions during the previous iterations. Moreover, the VCAES includes a mechanism to adaptively allocate evolution opportunities to each constructed group of decision variables. Thus, the decision variables with a strong impact on population advancement are assigned more evolution opportunities to accelerate population to approximate the Pareto-optimal fronts. To verify the effectiveness of the proposed VCAES, we carry out extensive numerical experiments on real-world workflows and cloud platforms to compare it with four representative algorithms. The numerical results demonstrate the superiority of the VCAES in resolving cloud workflow scheduling problems.

Co-evolution of neural architectures and features for stock market forecasting: A multi-objective decision perspective

In a multi-objective setting, a portfolio manager’s highly consequential decisions can benefit from assessing alternative forecasting models of stock index movement. The present investigation proposes a new approach to identify a set of non-dominated neural network models for further selection by the decision-maker. A new co-evolution approach is proposed to simultaneously select the features and topology of neural networks (collectively referred to as neural architecture), where the features are viewed from a topological perspective as input neurons. Further, the co-evolution is posed as a multi-criteria problem to evolve sparse and efficacious neural architectures. The well-known dominance and decomposition based multi-objective evolutionary algorithms are augmented with a non-geometric crossover operator to diversify and balance the search for neural architectures across conflicting criteria. Moreover, the co-evolution is augmented to accommodate the data-based implications of distinct market behaviors prior to and during the ongoing COVID-19 pandemic. A detailed comparative evaluation is carried out with the conventional sequential approach of feature selection followed by neural topology design, as well as a scalarized co-evolution approach. The results on three market indices (NASDAQ, NYSE, and S&P500) in pre- and peri-COVID time windows convincingly demonstrate that the proposed co-evolution approach can evolve a set of non-dominated neural forecasting models with better generalization capabilities.

Adaptive cooperative coevolutionary differential evolution for parallel feature selection in high-dimensional datasets

In many fields, it is a common practice to collect large amounts of data characterized by a high number of features. These datasets are at the core of modern applications of supervised machine learning, where the goal is to create an automatic classifier for newly presented data. However, it is well known that the presence of irrelevant features in a dataset can make the learning phase harder and, most importantly, can lead to suboptimal classifiers. Consequently, it is becoming increasingly important to be able to select the right subset of features. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features because of the poor scalability of optimization algorithms. In this article, we address the problem using a cooperative coevolutionary approach based on differential evolution. In the proposed algorithm, parallelized for execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adjusting the population size during the optimization results in significant performance improvements. A numerical investigation on some high-dimensional and medium-dimensional datasets shows that, in most cases, the proposed approach can achieve higher classification performance than other state-of-the-art methods.

Coevolutionary strategies at the collective level for improved generalism

Abstract In many complex practical optimization cases, the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialized approach to each application. The previously developed multilevel selection genetic algorithm (MLSGA) already shows good performance on a range of problems due to its diversity-first approach, which is rare among evolutionary algorithms. To increase the generality of its performance, this paper proposes utilization of multiple distinct evolutionary strategies simultaneously, similarly to algorithm selection, but with coevolutionary mechanisms between the subpopulations. This distinctive approach to coevolution provides less regular communication between subpopulations with competition between collectives rather than individuals. This encourages the collectives to act more independently creating a unique subregional search, leading to the development of coevolutionary MLSGA (cMLSGA). To test this methodology, nine genetic algorithms are selected to generate several variants of cMLSGA, which incorporates these approaches at the individual level. The mechanisms are tested on 100 different functions and benchmarked against the 9 state-of-the-art competitors to evaluate the generality of each approach. The results show that the diversity divergence in the principles of working of the selected coevolutionary approaches is more important than their individual performances. The proposed methodology has the most uniform performance on the divergent problem types, from across the tested state of the art, leading to an algorithm more likely to solve complex problems with limited knowledge about the search space, but is outperformed by more specialized solvers on simpler benchmarking studies.

Evolution-by-Coevolution of Neural Networks for Audio Classification

Neural networks are increasingly used in recognition problems, including static and moving images, sounds, etc. Unfortunately, the selection of optimal neural network architecture for a specific recognition problem is a difficult task, which often has an experimental nature. In this paper we present the use of evolutionary algorithms to obtain optimal architectures of neural networks used for audio sample classification. We extend the Pytorch DNN Evolution tool implementing co-evolutionary algorithms which create groups of neural networks that solve a given problem with a certain accuracy, with the support for problems in which training data consists of audio samples. In this paper we use the co-evolutionary approach to solve a sample sound classification problem. We describe how the sound data was prepared for processing with the use of the Mel Frequency Cepstral Coefficients (MFCC). Next we present the results of experiments conducted with the AudioMnist dataset. The obtained neural network architectures, whose classification accuracy is comparable to the classification accuracy attained by the AlexNet neural network, and their implications are discussed.

Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach

Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach

КО-ЭВОЛЮЦИОННЫЙ АЛГОРИТМ РАЗМЕЩЕНИЯ НА ОСНОВЕ ВЗАИМОДЕЙСТВИЯ СУБПОПУЛЯЦИЙ, ОТЛИЧАЮЩИХСЯ СТРАТЕГИЯМИ ПОИСКА

A new methodology and method for placing VLSI elements has been developed, which differin that the solution of the placement problem is based on the use of a fixed order of position selection,focused on the effective solution of the placement problem, and a heuristic procedure fordistributing elements by positions, which reduces the overall complexity and improves the qualityof the solution. The process of forming a list of positions on the switching field is carried out usingthe mechanisms of the wave algorithm. The choice of the final list is based on the principle of constructinga route with a minimum estimate of the total linear length of distances between routepositions. To solve the placement problem, a search algorithm based on the modified ant colonymethod has been developed. To exclude premature convergence and localization of the globalextremum of the problem, the development of the algorithm was carried out on the basis of the coevolutionaryapproach. The architecture of the co-evolutionary placement algorithm is developedon the basis of the ant colony algorithm paradigm. In the search space, sub-populations implementfour optimization strategies in parallel. In the work, the coevolution process is implemented on thebasis of the interaction of subpopulations that differ in search strategies. A distinctive feature ofthe co-evolutionary approach used is that subpopulations of solutions are actually virtual. Theprocess of co-evolution is implemented by one population of agents Z by sequential formation andmerging of virtual subpopulations of solutions into one population. In this paper, the solution ofthe placement problem is aimed at improving traceability by minimizing the resources required toimplement connections. A significant contribution to minimizing the spatial and temporal complexityof the search procedure was made by: the use by virtual sub-populations of a common evolutionarymemory, a common solution search graph, the formation of a single interpretation of thesolution in the form of a route on a complete directed graph with binary directed edges. Testingwas carried out on benchmarks 19s, PrimGA1, PrimGA2. The results compared to existing algorithmsare improved by 7-8%. The probability of obtaining a global optimum was 0.96. On average,solutions differ from the optimal by less than 1.5%. The time complexity of the algorithm forfixed values of the population size and the number of generations is O(n). The total time complexityof the hybrid algorithm is O(n2)−O(n3).

A Novel Self-Adaptive Cooperative Coevolution Algorithm for Solving Continuous Large-Scale Global Optimization Problems

Unconstrained continuous large-scale global optimization (LSGO) is still a challenging task for a wide range of modern metaheuristic approaches. A cooperative coevolution approach is a good tool for increasing the performance of an evolutionary algorithm in solving high-dimensional optimization problems. However, the performance of cooperative coevolution approaches for LSGO depends significantly on the problem decomposition, namely, on the number of subcomponents and on how variables are grouped in these subcomponents. Also, the choice of the population size is still an open question for population-based algorithms. This paper discusses a method for selecting the number of subcomponents and the population size during the optimization process (“on fly”) from a predefined pool of parameters. The selection of the parameters is based on their performance in the previous optimization steps. The main goal of the study is the improvement of coevolutionary decomposition-based algorithms for solving LSGO problems. In this paper, we propose a novel self-adapt evolutionary algorithm for solving continuous LSGO problems. We have tested this algorithm on 15 optimization problems from the IEEE LSGO CEC’2013 benchmark suite. The proposed approach, on average, outperforms cooperative coevolution algorithms with a static number of subcomponents and a static number of individuals.