• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3133-3151
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• 2012

The optimal price strategy selection of two bounded rational cognitive mobile virtual network operators (MVNOs) in a duopoly spectrum sharing market is investigated. The bounded rational operators dynamically compete to sell the leased spectrum to secondary users in order to maximize their profits. Meanwhile, the secondary users' heterogeneous preferences to rate and price are taken into consideration. The evolutionary game theory (EGT) is employed to model the dynamic price strategy selection of the MVNOs taking into account the response of the secondary users. The behavior dynamics and the evolutionary stable strategy (ESS) of the operators are derived via replicated dynamics. Furthermore, a reward and punishment mechanism is developed to optimize the performance of the operators. Numerical results show that the proposed evolutionary algorithm is convergent to the ESS, and the incentive mechanism increases the profits of the operators. It may provide some insight about the optimal price strategy selection for MVNOs in the next generation cognitive wireless networks.

• Geomechanics and Engineering
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• v.13 no.5
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• pp.845-860
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• 2017

It is very important to study displacement prediction in geotechnical engineering. Nowadays, the grey system method, time series analysis method and artificial neural network method are three main methods. Based on the brief introduction, the three methods are analyzed comprehensively. Their merits and demerits, applied ranges are revealed. To solve the shortcomings of the artificial neural network method, a new prediction method based on new evolutionary neural network is proposed. Finally, through two real engineering applications, the analysis of three main methods and the new evolutionary neural network method all have been verified. The results show that, the grey system method is a kind of exponential approximation to displacement sequence, and time series analysis is linear autoregression approximation, while artificial neural network is nonlinear autoregression approximation. Thus, the grey system method can suitably analyze the sequence, which has the exponential law, the time series method can suitably analyze the random sequence and the neural network method almostly can be applied in any sequences. Moreover, the prediction results of new evolutionary neural network method is the best, and its approximation sequence and the generalization prediction sequence are all coincided with the real displacement sequence well. Thus, the new evolutionary neural network method is an acceptable method to predict the measurement displacements of geotechnical engineering.

• The Plant Pathology Journal
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• v.35 no.5
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• pp.498-507
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• 2019

Odontoglossum ringspot virus (ORSV) is a member of the genus Tobamovirus. It is one of the most prevalent viruses infecting orchids worldwide. Earlier studies reported the genetic variability of ORSV isolates from Korea and China. However, the evolutionary rate, timescale, and phylogeographical analyses of ORSV were unclear. Twenty-one coat protein (CP) gene sequences of ORSV were determined in this study, and used them together with 145 CP sequences obtained from GenBank to infer the genetic diversities, evolutionary rate, timescale and migration of ORSV populations. Evolutionary rate of ORSV populations was $1.25{\times}10^{-3}nucleotides/site/y$. The most recent common ancestors came from 30 year ago (95% confidence intervals, 26-40). Based on CP gene, ORSV migrated from mainland China and South Korea to Taiwan island, Germany, Australia, Singapore, and Indonesia, and it also circulated within east Asia. Our study is the first attempt to evaluate the evolutionary rates, timescales and migration dynamics of ORSV.

• Nuclear Engineering and Technology
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• v.54 no.10
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• pp.3864-3877
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• 2022

We propose a new approach called PESA (Prioritized replay Evolutionary and Swarm Algorithms) combining prioritized replay of reinforcement learning with hybrid evolutionary algorithms. PESA hybridizes different evolutionary and swarm algorithms such as particle swarm optimization, evolution strategies, simulated annealing, and differential evolution, with a modular approach to account for other algorithms. PESA hybridizes three algorithms by storing their solutions in a shared replay memory, then applying prioritized replay to redistribute data between the integral algorithms in frequent form based on their fitness and priority values, which significantly enhances sample diversity and algorithm exploration. Additionally, greedy replay is used implicitly to improve PESA exploitation close to the end of evolution. PESA features in balancing exploration and exploitation during search and the parallel computing result in an agnostic excellent performance over a wide range of experiments and problems presented in this work. PESA also shows very good scalability with number of processors in solving an expensive problem of optimizing nuclear fuel in nuclear power plants. PESA's competitive performance and modularity over all experiments allow it to join the family of evolutionary algorithms as a new hybrid algorithm; unleashing the power of parallel computing for expensive optimization.

• Korean Journal of Biological Psychiatry
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• v.29 no.1
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• pp.1-8
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• 2022

Objectives In regard to the social competition hypothesis, depression is viewed as an involuntary defeat strategy. A previous study has demonstrated that adaptation in microenvironments can result in a wide range of behavioural patterns including defense activation disorders. Using a simulation model with evolutionary ecological agents, we explore how the fitness of various defence activation traits has changed over time in different environments with high and low social mobility. Methods The Evolutionary Ecological Model of Defence Activation Disorder, which is based on the Marginal Value Theorem, was used to examine changes in relative fitness for individuals with defensive activation disorders after adjusting for social mobility. Results Our study examined the effects of social mobility on fitness by varying the d-values, a measure of depression in the model. With a decline in social mobility, the level of fitness of individuals with high levels of defense activation decreased. We gained insight into the evolutionary influence of varying levels of social mobility on individuals' degrees of depression. In the context of a highly stratified society, the results support a mismatch hypothesis which states that high levels of defence are detrimental. Conclusions Despite the fact that niche specialization in habitats composed of multiple microenvironments can result in diverse levels of defensive activation being evolutionary strategies for stability, decreased social mobility may lead to a decrease in fitness of individuals with highly activated defence modules. There may be a reason behind the epidemic of depression in modern society.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.6
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• pp.491-496
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• 2002

Multi-objective Optimization Problems(MOPs) are occur more frequently than generally thought when we try to solve engineering problems. In the real world, the majority cases of optimization problems are the problems composed of several competitive objective functions. In this paper, we introduce the definition of MOPs and several approaches to solve these problems. In the introduction, established optimization algorithms based on the concept of Pareto optimal solution are introduced. And contrary these algorithms, we introduce theoretical backgrounds of Nash Genetic Algorithm(Nash GA) and Evolutionary Stable Strategy(ESS), which is the basis of Co-evolutionary algorithm proposed in this paper. In the next chapter, we introduce the definitions of MOPs and Pareto optimal solution. And the architecture of Nash GA and Co-evolutionary algorithm for solving MOPs are following. Finally from the experimental results we confirm that two algorithms based on Evolutionary Game Theory(EGT) which are Nash GA and Co-evolutionary algorithm can search optimal solutions of MOPs.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.509-526
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• 2008

This paper aims to examine the evolutionary dynamics of the Korea's footwear industry by adopting evolutionary perspectives. To explain the evolutionary dynamics of an industry, evolutionary perspectives have paid a particular attention to exploring a variety of factors for influencing the evolution of the industry, such as the selection and imitation of the firm, the mechanism of firm's entry and exit, technological characteristics and innovation processes. The majority of existing research tend to explain that the decline of the Korea's footwear industry since 1990 was mostly due to the rapid rising of wage and the structural changes in labor-intensive industries. On the contrary, this paper attempts to explain the decline of the Korea's footwear industry, in terms of the path of selection and imitation, the dominant technological paradigm, regulatory frameworks and the meso trajectory of industry evolution. This paper concludes that the decline of the Korea's footwear industry since 1990 was appeared as a result of the evolutionary selection processes of the firms in order to adapt to changes in the environment of competition and the regime of market selection in the global footwear industry.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.841-848
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• 2004

Evolutionary a1gorithms are well-suited for multi-objective optimization problems involving several, often conflicting objectives. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. However, generalized evolutionary multi-objective optimization algorithms have a weak point, in which the distribution of solutions are not uni-formly distributed onto Pareto optimal front. In this paper, we propose an evolutionary a1gorithm for multi-objective optimization which uses seed individuals in order to overcome weakness of algorithms Published. Seed individual means a solution which is not located in the crowded region on Pareto front. And the idea of our algorithm uses seed individuals for reproducing individuals for next generation. Thus, proposed a1go-rithm takes advantage of local searching effect because new individuals are produced near the seed individual with high probability, and is able to produce comparatively uniform distributed pareto optimal solutions. Simulation results on five testbed problems show that the proposed algo-rithm could produce uniform distributed solutions onto pareto optimal front, and is able to show better convergence compared to NSGA-II on all testbed problems except multi-modal problem.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.213-220
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• 2004

Evolutionary algorithms are well-suited for multi-objective optimization problems involving several. often conflicting objective. Pareto-based evolutionary algorithms, in particular, have shown better performance than other multi-objective evolutionary algorithms in comparison. Recently, pareto-based evolutionary algorithms uses a density information in fitness assignment scheme for generating uniform distributed global pareto optimal front. However, the usage of density information is not Important elements in a whole evolution path but plays an auxiliary role in order to make uniform distribution. In this paper, we propose an evolutionary algorithms for multi-objective optimization which assigns the fitness using pareto dominance rank and density weighting, and thus pareto dominance rank and density have similar influence on the whole evolution path. Furthermore, the experimental results, which applied our method to the six multi-objective optimization problems, show that the proposed algorithms show more promising results.

• Journal of The Korean Association For Science Education
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• v.23 no.5
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• pp.470-483
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• 2003

This study analyzed the evolutionary content in 13 textbooks developed from the first to the 6th high school biology curriculum, The content analysis of textbooks, which were delineated nine component, was performed on the 80 evolutionary categories, According to the result, the proportion of the total evolutionary content in textbook increased from the textbooks developed by the Ist curriculum to the textbooks developed by the 6th curriculum, but the proportion of 'main narrative' in total evolutionary content was gradually decreased. It also showed that biology curriculum and points of view of textbook writers influenced on the proportion of evolutionary contents. On the whole, the topics of analysed textbooks exhibit insufficient diversity, Any categories- 'group selection', 'gene selection', 'gaps in fossil record', 'co-evolution', 'punctuated equilibrium', 'mosaic evolution', 'place of labor in human evolution', 'human race differentiation', 'criticism of "ontogeny recapitulates phylogeny", and 'human activities affecting evolution' - were not treated and others - 'theory of neutralism', 'theories of major episodes(excepting extinctions) found in the geologic time scale', 'sympatric speciation', 'clinal and area-effect speciation', 'polyploidy and evolution', 'gradualism' and 'evolution and origin of mammals' - were treated very lightly, the most emphasized topic was 'phylogeny in general' and 'formation of precells', 'miscellaneous' in the order of emphasis. 'Theory of natural selection' was lightly treated as just one of evolutionary theory though it should be emphasized as major theme of evolution. Also, the law of recapitulation, of which biologists doubt the validity, was discussed as an evidence of evolution in some textbooks. And the agents of genetic equilibrium disruption like genetic drift and migration were treated as of little importance. On the basis of above result, it was suggested that the textbook writers introduced the more meaningful evolutionary topics focused the theory of natural selection in explanation of evolution and evolution theory.