Figure 3
The maximum CA performance
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We present a comparative study of an evolutionary and a coevolutionary search model. In the latter, strategies for solving a problem coevolve with training cases. We find that the coevolutionary model has a relatively large efficacy: 41 out of 50 (82%) of the simulations produce high quality strategies. In contrast, the evolutionary model has a ver...
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... An early endeavour in this area is the phantom parasite, which marginally reduces the fitness of an unbeatable competitor, while all other fitness values remain unchanged [20]. Later, the Φ function was introduced for the density classification task to coevolve cellular automata rules that classify the density of an initial condition [21]. The Φ function translates all fitness values such that individuals are rewarded most highly for being equally difficult and easy to classify (i.e., by being classified correctly half of the time); while individuals that are always classified or always unclassified are punished with low fitness. ...
This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain independent and requires no calibration. We first perform a controlled comparative evaluation of SF’s ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF performs similarly to alternative techniques presented in the literature but has the advantage of requiring no parameter tuning. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain a better trade-off between engagement and performance than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.
... باشند. نشان دهنده است. مختلط مزدوج ی و مادر موجک یا پنجره تابعPagie and Mitchell, 2002;Rosin and Belew, 1995;Wiegand and Sarma, 2004 می صورت هستند دار خط تقسیم از قبل گیرد. داده بتواند ماشین اینکه برای ی، دسته را باال پیچیدگی با های بندی داده کند، به ها تابع وسیله phi باالتر خیلی ابعاد با فضای به می برده به شود. ...
The impact of the dust phenomenon in Iran is so vast that it has involved more than half of the country's provinces in some way with the issues and limitations of this natural phenomenon, which, in addition to the environmental effects, has disrupted the implementation of national sustainable development plans and So far, it has and will have many negative consequences. This research tries to present a new hybrid model using artificial intelligence hybrid metamodels as well as Box Jenkins hybrid metamodels to predict and model the FDSD index (frequency of days with dust storms), in seven synoptic stations of Khuzestan province with length The statistical period has been 40 years (1981-2020). The hybrid prediction algorithms used in this research include W-ANFIS, AF-SVM, ARIMA-NARX and SARIMA-SETAR. The prediction results showed that the decrease in the performance of hybrid models to predict the FDSD index has a direct relationship with the decrease in the frequency of days with dust storms. So that the correlation coefficient for experimental data in AF-SVM and W-ANFIS hypermodels from 0.991 and 0.985 to 0.985 and 0.958, respectively, and Nash Sutcliffe coefficient has also decreased from 0.977 and 0.960 to 0.973 and 0.952, respectively. Also, the RMSE coefficient from Abadan station to Dezful for the two metamodels from 0.135 and 0.151 to 0.140 and 0.179 respectively, And the MAE coefficient has also increased from 0.054 and 0.068 to 0.060 and 0.093, respectively. Correlation coefficient for test data in Box Jenkins SARIMA-SETAR and ARIMA-NARX hypermodels also from 0.967 and 0.951 to 0.958 and 0.941 respectively and the Nash Sutcliffe coefficient has also decreased from 0.945 and 0.923 to 0.938 and 0.913, respectively, which indicates the weakening of the performance of hybrid metamodels with the decrease in the frequency of dust storms in Khuzestan province.
... An early endeavour in this area is the phantom parasite, which marginally reduces the fitness of an unbeatable competitor, while all other fitness values remain unchanged [21]. Later, the Φ function was introduced for the density classification task to coevolve cellular automata rules that classify the density of an initial condition [17]. The Φ function translates all fitness values such that individuals are rewarded most highly for being equally difficult and easy to classify (i.e., by being classified correctly half of the time); while individuals that are always classified or always unclassified are punished with low fitness. ...
This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain-independent and requires no calibration. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF is able to maintain engagement better than other techniques in the literature. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.
... An early endeavour in this area is the phantom parasite, which marginally reduces the fitness of an unbeatable competitor, while all other fitness values remain unchanged (Rosin, 1997). Later, the Φ function was introduced for the density classification task to coevolve cellular automata rules that classify the density of an initial condition (Pagie and Mitchel, 2002). The Φ function translates all fitness values such that individuals are rewarded most highly for being equally difficult and easy to classify (i.e., by being classified correctly half of the time); while individuals that are always classified or always unclassified are punished with low fitness. ...
We propose substitution of the fittest (SF), a novel technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. The approach presented is domain-independent and requires no calibration. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions. Results demonstrate that the solution discovery performance of SF is comparable with other techniques in the literature, while SF also offers benefits including a greater ability to maintain engagement and a much simpler mechanism.
... Competition and cooperation simultaneously exist in a business ecosystem. Noteworthy relevant concepts that came from the complexity theory to the business ecosystems theory are self-organization (Mitleton-Kelly 2003), emergence (Mitleton-Kelly 2004), coevolution (Pagie and Mitchell, 2002), and adaptation (Merry, 1999). Business ecosystems are said to grow through self-organization, emergence, and coevolution. ...
Cite/Reference as:
Hoveskog, M., and Halila, F. (eds), (2021). Proceedings of the 6th International Conference on New Business Models: New Business Models in a Decade of Action: Sustainable, Evidence-based, Impactful. Halmstad: Halmstad University Press. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44872
... Competition and cooperation simultaneously exist in a business ecosystem. Noteworthy relevant concepts that came from the complexity theory to the business ecosystems theory are self-organization (Mitleton-Kelly 2003), emergence (Mitleton-Kelly 2004), coevolution (Pagie and Mitchell, 2002), and adaptation (Merry, 1999). Business ecosystems are said to grow through self-organization, emergence, and coevolution. ...
Purpose: The purpose of this paper is to introduce the value proposition and structure of the business ecosystem of peer-to-peer electricity trading through a future oriented approach. Design/Methodology/Approach: This study follows a qualitative approach. It conducts conceptual analyses by utilizing previously validated tools in similar contexts. First, different views on business ecosystems are introduced and an argument is made to justify an ecosystem perspective for peer-to-peer electricity trading. Second, the value proposition of the peer-to-peer electricity trading ecosystem is identified by utilising a meta-model which consists of four elements: end customer value, business value (shareholder value), collaborative value (business value to the supply chain) and societal value (value creation in the supply chain and control of negative externalities). Third, based on the structural view of business ecosystems, the study identifies actors, positions, links, and activities in the traditional electricity trading. And last, (structural) changes of the ecosystem for peer-to-peer electricity trading are discussed. Findings: This paper elaborates the business ecosystem of peer-to-peer electricity trading and highlights the structural changes it imposes to the status quo. Practical and social implications: The ecosystem construct adds insights into actors' ecosystem strategy regarding their business models for peer-to-peer electricity trading as well as into the governance of this type of trading. It provides a comprehensive view for policy makers. It enhances the research designs in detailed aspects of the peer-to-peer electricity trading by providing a wide lense. Originality/Value: The identified business ecosystem of peer-to-peer electricity trading provides a comprehensive, multi-stakeholder perspective to incorporate complexities and include externalities.
... There are 3 behavior patterns: development behavior, distribution behavior, and selection behavior. [20][21][22] Development behavior means flora development for adaptation to environmental behavior. [23][24][25] Distribution behavior stands for the movement of grains. ...
In this study, the hybrid support vector machine–artificial flora algorithm method was developed and the obtained results were compared with those of the support vector–wave vector machine model. Karkheh catchment area was considered as a case study to estimate the flow rate of rivers using the daily discharge statistics taken from hydrometric stations located upstream of the dam in the statistical period of 2008 to 2018. Necessary criteria including coefficient of determination, root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe coefficient were used to evaluate and compare the models. The results illustrated that the combined structures provided acceptable results in terms of river flow modeling. Also, a comparison of the models based on the evaluation criteria and Taylor’s diagram demonstrated that the proposed hybrid method with the correlation coefficient of R ² = 0.924 to 0.974, RMSE = 0.022 to 0.066 m ³ /s, MAE = 0.011 to 0.034 m ³ /s, and Nash-Sutcliffe (NS) coefficient = 0.947 to 0.986 outperformed other methods in terms of estimating the daily flow rates of rivers.
... The existing study of coordinated IOP Conf. Series: Earth and Environmental Science 585 (2020) 012018 IOP Publishing doi:10.1088/1755-1315/585/1/012018 2 energy development mainly includes three aspects: firstly the study of supply and demand relations of different energy types and energy system coordination and optimization from the perspective of technology, secondly the study of energy system simulation from the perspective of planning [4][5][6], and thirdly the establishment of indicator system to evaluate the synergetic development from the perspective of performance [7][8][9]. However, few of them have probed into the comprehensive energy system or analyzed the impact mechanism of energy relations, and most of the evaluation indicators are qualitative instead of quantitative [10]. ...
With the development of ecological civilization construction and regional coordinated development strategy in China, to adapt to the development trend of urban agglomeration, the energy structure will be adjusted and upgraded accordingly. The energy structure has gradually changed from fossil energy to diversified supply mode. The integrated energy system of urban agglomeration will coordinate with energy planning, operation, management and other links, and realize the linkage and complementarity of multiple energy sources. In this context, it is necessary to carry out a systematic analysis of the comprehensive energy system of urban agglomerations. This paper analyzes the connotation, characteristics and function mechanism of comprehensive energy system of urban agglomeration, and constructs a set of evaluation index system. These research ideas and methods can be applied to the scenarios of energy development, utilization and management that adapt to the development of urban agglomeration, and provide scientific analysis tools for energy system management and evaluation.
... Also in Mitchell et al. [24] and Mitchell et al. [25] , the authors exploited genetic algorithms for evolving cellular automata for specific computational tasks such as density classification and synchronization. Further studies can be found in Barmpoutis [4] , Giacobini et al. [12] , Pagie and Mitchell [31] , Sipper and Ruppin [37] , Sipper and Tomassini [39] . ...
Cellular automata are systems which use a rule to describe the evolution of a population in a discrete lattice, while genetic algorithms are procedures designed to find solutions to optimization problems inspired by the process of natural selection. In this paper, we introduce an original implementation of a cellular automaton whose rules use a fitness function to select for each cell the best mate to reproduce and a crossover operator to determine the resulting offspring. This new system, with a proper definition, can be both a cellular automaton and a genetic algorithm. We show that in our system the Conway’s Game of Life can be easily implemented and, consequently, it is capable of universal computing. Moreover two generalizations of the Game of Life are created and also implemented with it. Finally, we use our system for studying and implementing the prisoner’s dilemma and rock-paper-scissors games, showing very interesting behaviors and configurations (e.g., gliders) inside these games.
... Propagation distance refers to how far a seed can spread. There are three major behavioral patterns: evolution behavior, spreading behavior, and select behavior [41][42][43]. Evolution behavior means there is a probability that the plant will evolve to adapt to the environment [44][45][46]. Spreading behavior refers to the movement of seeds, and seeds can move through autochory or allochory. ...
Inspired by the process of migration and reproduction of flora, this paper proposes a novel artificial flora (AF) algorithm. This algorithm can be used to solve some complex, non-linear, discrete optimization problems. Although a plant cannot move, it can spread seeds within a certain range to let offspring to find the most suitable environment. The stochastic process is easy to copy, and the spreading space is vast; therefore, it is suitable for applying in intelligent optimization algorithm. First, the algorithm randomly generates the original plant, including its position and the propagation distance. Then, the position and the propagation distance of the original plant as parameters are substituted in the propagation function to generate offspring plants. Finally, the optimal offspring is selected as a new original plant through the selection function. The previous original plant becomes the former plant. The iteration continues until we find out optimal solution. In this paper, six classical evaluation functions are used as the benchmark functions. The simulation results show that proposed algorithm has high accuracy and stability compared with the classical particle swarm optimization and artificial bee colony algorithm.
