• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.80-96
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• 2022

Nowadays, artificial intelligence promotes the rapid development of skin cancer detection technology, and the federated skin cancer detection model (FSDM) and dual generative adversarial network model (DGANM) solves the fragmentation and privacy of data to a certain extent. To overcome the problem that the many-objective evolutionary algorithm (MaOEA) cannot guarantee the convergence and diversity of the population when solving the above models, a many-objective evolutionary algorithm based on integrated strategy (MaOEA-IS) is proposed. First, the idea of federated learning is introduced into population mutation, the new parents are generated through sub-populations employs different mating selection operators. Then, the distance between each solution to the ideal point (SID) and the Achievement Scalarizing Function (ASF) value of each solution are considered comprehensively for environment selection, meanwhile, the elimination mechanism is used to carry out the select offspring operation. Eventually, the FSDM and DGANM are solved through MaOEA-IS. The experimental results show that the MaOEA-IS has better convergence and diversity, and it has superior performance in solving the FSDM and DGANM. The proposed MaOEA-IS provides more reasonable solutions scheme for many scholars of skin cancer detection and promotes the progress of intelligent medicine.

• 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 Bulletin of The Korean Astronomical Society
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• v.42 no.1
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• pp.52.1-52.1
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• 2017

Galaxy clusters are the most massive gravitationally bound systems and thus probably the most recent objects to form. One of promising routes to understand the assembly history of galaxy clusters is to measure observable quantities of components in clusters that are sensitive to the evolutionary state of the cluster. Recent deep observations on the nearby clusters show distinct diffuse intracluster light (ICL), that the light from stars are not bound any individual cluster galaxy, however until now this component has not been well studied due to its faint nature, with typical brightness of ~100 times fainter than the sky background. As shown in galaxy cluster simulation studies, the ICL abundance increases during various dynamical exchanges of galaxies such as the disruption of dwarf galaxies, major mergers between galaxies and the tidal stripping of galaxies. Thus, the ICL is an effective tool to measure the evolutionary stage of galaxy clusters. Moreover, the investigation of the ICL evolution mechanism will allow us understand the galaxy evolution process therein. In this pilot study, we target the Coma cluster, where the existing ICL studies are limited only in the central region. With large and uniform deep optical images from the Subaru telescope, available only recently (Okabe et al. 2014), we are developing a robust ICL measurement technique, extracting the ICL surface brightness and color profiles, which will allow us to study the origin of the ICL and its connection to the evolutionary history of the Coma cluster. For the next phase, we plan to utilize the plenty of spectroscopy data from the MMT telescope to compare ICL properties with the star formation history of the brightest cluster galaxies (BCG), and discuss the ICL formation mechanism of the Coma cluster by comparing the distribution of cluster galaxies with the distribution of diffuse light inside the Coma cluster.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.328-338
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• 2006

A new evolutionary computation technique, called particle swarm optimization(PSO), has been proposed and introduced recently. PSO has been inspired by the social behavior of flocking organisms, such as swarms of birds and fish schools and PSO is an algorithm that follows a collaborative population-based search model. Each particle of swarm flies around in a multidimensional search space looking for the optimal solution. Then, Particles adjust their position according to their own and their neighboring-particles experience. In this paper, characteristics of PSO such as mentioned are reviewed and compared with GA which is based on the evolutionary mechanism in natural selection. Also dimensionalities of PSO and GA are compared throughout numeric experimental studies. The comparative studies demonstrate that PSO is characterized as simple in concept, easy to implement, and computationally efficient and can generate a high-quality solution and stable convergence characteristic than GA.

• Genomics & Informatics
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• v.8 no.4
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• pp.170-176
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• 2010

The Ribonucleotide reductases (RNR) are essential enzymes that catalyze the conversion of nucleotides to deoxynucleotides in DNA replication and repair in all living organisms. The RNRs operate by a free radical mechanism but differ in the composition of subunit, cofactor required and regulation by allostery. Based on these differences the RNRs are classified into three classesclass I, class II and class III which depend on oxygen, adenosylcobalamin and S-adenosylmethionine with an iron sulfur cluster respectively for radical generation. In this article thirty seven sequences belonging to each of the three classes of RNR were analyzed by using various tools of bioinformatics. Phylogenetic analysis, dot-plot comparisons and motif analysis was done to identify a number of differences in the three classes of RNRs. In this research article, we have attempted to decipher evolutionary relationship between the three classes of RNR by using bioinformatics approach.

• ETRI Journal
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• v.33 no.1
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• pp.39-49
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• 2011

A new structure learning approach for Bayesian networks based on asexual reproduction optimization (ARO) is proposed in this paper. ARO can be considered an evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. In ARO, a parent produces a bud through a reproduction operator; thereafter, the parent and its bud compete to survive according to a performance index obtained from the underlying objective function of the optimization problem: This leads to the fitter individual. The convergence measure of ARO is analyzed. The proposed method is applied to real-world and benchmark applications, while its effectiveness is demonstrated through computer simulations. Results of simulations show that ARO outperforms genetic algorithm (GA) because ARO results in a good structure and fast convergence rate in comparison with GA.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.365-370
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• 2001

The genetic algorithm that simulates the virus evolutionary theory has been developed applying to combinatorial optimization problems. The algorithm in this study uses only one individual and a population of viruses. The individual is attacked, inflected and improved by the viruses. The viruses are composed of flour genes (a pair of top gene and a pair of tail gene). If the individual is improved by the attacking, the inflection occurs. After the infection, the tail genes are mutated. If the same virus attacks several times and fails to inflect, the top genes of the virus are mutated. By this mutation, the individual can be improved effectively. In addition, the influence of the immunologic mechanism on evolution is simulated.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.19 no.6
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• pp.826-833
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• 2010

Electric-thermal-structural actuated compliant mechanisms are mechanisms onto which electric voltage drop is applied as input instead of force. This mechanism is based on thermal expansion of material while being heated. Compliant mechanisms are designed subjected to electric charge input using BESO(bi-directional evolutionary structural optimization) method. Reliability-based topology optimization (RBTO) is applied to the topology design of actuators. performance measure approach (PMA), which has probabilistic constraints that are formulated in terms of the reliability index, is adopted to evaluate the probabilistic constraints. In this study, BESO method is used to obtain optimal topology of compliant mechanisms from initial design domain. PMA approach is used to evaluate reliability index. The procedure has been tested in numerical applications and compared with the results obtained by other methods to validate these approaches.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.108-113
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• 2005

This paper suggests a control method for an efficient topology/parameter evolution in a bond graph-based GP design framework that automatically synthesizes designs for multi-domain, lumped parameter dynamic systems. We adopt a hierarchical breeding control mechanism with fitness-level-dependent differences to obtain better balancing of topology/parameter search - biased toward topological changes at low fitness levels, and toward parameter changes at high fitness levels. As a testbed for this approach in bond graph synthesis, an eigenvalue assignment problem, which is to find bond graph models exhibiting minimal distance errors from target sets of eigenvalues, was tested and showed improved performance for various sets of eigenvalues.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.280-280
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• 2000

In this paper, we propose a method of constructing equation using bio-inspired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants. Here is. we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting mechanism. The DNA coding method has no limitation in expressing the production rule of L-system. Evolutionary algorithms motivated by Darwinian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it to one step ahead prediction of Mackey-Glass time series.