• Publications of The Korean Astronomical Society
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• v.30 no.2
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• pp.473-474
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• 2015

Halo merger trees are the essential backbone of semi-analytic models for galaxy formation and evolution. Srisawat et al. (2013) show that different tree building algorithms can build different halo merger histories from a numerical simulation for structure formation. In order to understand the differences induced by various tree building algorithms, we investigate the impact of halo merger trees on a semi-analytic model. We find that galaxy properties in our models show differences between trees when using a common parameter set. The models independently calibrated for each tree can reduce the discrepancies between global galaxy properties at z=0. Conversely, with regard to the evolutionary features of galaxies, the calibration slightly increases the differences between trees. Therefore, halo merger trees extracted from a common numerical simulation using different, but reliable, algorithms can result in different galaxy properties in the semi-analytic model. Considering the uncertainties in baryonic physics governing galaxy formation and evolution, however, these differences may not necessarily be significant.

• Communications for Statistical Applications and Methods
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• v.27 no.3
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• pp.385-395
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• 2020

We present a new maximum likelihood approach to estimate demographic history using genomic data sampled from two populations. A demographic model such as an isolation-with-migration (IM) model explains the genetic divergence of two populations split away from their common ancestral population. The standard probability model for an IM model contains a latent variable called genealogy that represents gene-specific evolutionary paths and links the genetic data to the IM model. Under an IM model, a genealogy consists of two kinds of evolutionary paths of genetic data: vertical inheritance paths (coalescent events) through generations and horizontal paths (migration events) between populations. The computational complexity of the IM model inference is one of the major limitations to analyze genomic data. We propose a fast maximum likelihood approach to estimate IM models from genomic data. The first step analyzes genomic data and maximizes the likelihood of a coalescent tree that contains vertical paths of genealogy. The second step analyzes the estimated coalescent trees and finds the parameter values of an IM model, which maximizes the distribution of the coalescent trees after taking account of possible migration events. We evaluate the performance of the new method by analyses of simulated data and genomic data from two subspecies of common chimpanzees in Africa.

• Parasites, Hosts and Diseases
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• v.41 no.4
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• pp.209-219
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• 2003

The evolutionary course of the CsRn1 long-terminal-repeat (LTR) retrotransposon was predicted by conducting a phylogenetic analysis with its paralog LTR sequences. Based on the clustering patterns in the phylogenetic tree, multiple CsRn1 copies could be grouped into four subsets, which were shown to have different integration times. Their differential sequence divergences and heterogeneous integration patterns strongly suggested that these subsets appeared sequentially in the genome of C. sinensis. Members of recently expanding subset showed the lowest level of divergence in their L TR and reverse transcriptase gene sequences. They were also shown to be highly polymorphic among individual genomes of the trematode. The CsRn1 element exhibited a preference for repetitive, agenic chromosomal regions in terms of selecting integration targets. Our results suggested that CsRn1 might induce a considerable degree of intergenomic variation and, thereby, have influenced the evolution of the C. sinensis genome.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.452-455
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• 1999

We introduce an evolutionary approach to on-line learning for mobile robot control using reconfigurable hardware. We use genetic programming as an evolutionary engine. Control programs are encoded in tree structure. Genetic operators, such as node mutation, adapt the program trees based on a set of training cases. This paper discusses the advantages and constraints of the evolvable hardware approach to robot learning and describes a FPGA implementation of the presented genetic programming method.

• Journal of Microbiology
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• v.38 no.2
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• pp.62-65
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• 2000

Partial sequence comparisons of mitochondrial small subunit rDNA (mt SSU rDNA) were used to examine taxonomic and evolutionary relationships among seven Penicillium species : two monoverticillate species, two biverticillate species, and three terverticillate species. Amplified fragments of mt SSU rDNA highly varied among seven species in size, suggesting the existence of multiple insertions or deletions in the region. A phylogengtic tree was constructed by exhaustive search of parsimony analysis. The phylogenetic tree distinguished two statistically supported monophyletic groups, one for two monoverticillate species and the other for three terverticillate species and ont biverticillate species, P. vulpinum. The phylogenetic relationship of P. waksmanii, the biverticillate species, was not clear.

• Computers and Concrete
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• v.19 no.6
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• pp.651-658
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• 2017

High-performance concrete, besides aggregate, cement, and water, incorporates supplementary cementitious materials, such as fly ash and blast furnace slag, and chemical admixture, such as superplasticizer. Hence, it is a highly complex material and modeling its behavior represents a difficult task. This paper presents an evolutionary system for the prediction of high performance concrete strength. The proposed framework blends a recently developed version of genetic programming with a local search method. The resulting system enables us to build a model that produces an accurate estimation of the considered parameter. Experimental results show the suitability of the proposed system for the prediction of concrete strength. The proposed method produces a lower error with respect to the state-of-the art technique. The paper provides two contributions: from the point of view of the high performance concrete strength prediction, a system able to outperform existing state-of-the-art techniques is defined; from the machine learning perspective, this case study shows that including a local searcher in the geometric semantic genetic programming system can speed up the convergence of the search process.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.2
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• pp.13-21
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• 1996

The necessity and usefulness of higher-order neural networks have been well-known since early days of neurocomputing. However the explosive number of terms has hampered the design and training of such networks. In this paper we present an evolutionary learning method for efficiently constructing problem-specific higher-order neural models. The crux of the method is the neural tree representation employing both sigma and pi units, in combination with the use of an MDL-based fitness function for learning minimal models. We provide experimental results in classification and prediction problems which demonstrate the effectiveness of the method. I. Introduction topology employs one hidden layer with full connectivity between neighboring layers. This structure has One of the most popular neural network models been very successful for many applications. However, used for supervised learning applications has been the they have some weaknesses. For instance, the fully mutilayer feedforward network. A commonly adopted connected structure is not necessarily a good topology unless the task contains a good predictor for the full *d*dWs %BH%W* input space.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.3
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• pp.1-8
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• 1998

This paper proposes a reinforcement genetic programming based on the reinforcement learning method for the performance improvement of genetic programming. Genetic programming which has tree structure program has much flexibility of problem expression because it has no limitation in the size of chromosome compared to the other evolutionary algorithms. But worse results on the point of convergence associated with mutation and crossover operations are often due to this characteristic. Therefore the sizes of population and maximum generation are typically larger than those of the other evolutionary algorithms. This paper proposes a new method that executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. The validity of the proposed method is evaluated by appling it to the artificial ant problem.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.6
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• pp.876-887
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• 2015

Dicer, an ribonuclease type III type endonuclease, is the key enzyme involved in biogenesis of microRNAs (miRNAs) and small interfering RNAs (siRNAs), and thus plays a critical role in RNA interference through post transcriptional regulation of gene expression. This enzyme has not been well studied in the Indian water buffalo, an important species known for disease resistance and high milk production. In this study, the primary coding sequence (5,778 bp) of bubaline dicer (GenBank: AB969677.1) was determined and the bubaline Dicer1 biocomputationally characterized to determine the phylogenetic signature among higher eukaryotes. The evolutionary tree revealed that all the transcript variants of Dicer1 belonging to a specific species were within the same node and the sequences belonging to primates, rodents and lagomorphs, avians and reptiles formed independent clusters. The bubaline dicer1 is closely related to that of cattle and other ruminants and significantly divergent from dicer of lower species such as tapeworm, sea urchin and fruit fly. Evolutionary divergence analysis conducted using MEGA6 software indicated that dicer has undergone purifying selection over the time. Seventeen divergent sequences, representing each of the families/taxa were selected to study the specific regions of positive vis-$\grave{a}$-vis negative selection using different models like single likelihood ancestor counting, fixed effects likelihood, and random effects likelihood of Datamonkey server. Comparative analysis of the domain structure revealed that Dicer1 is conserved across mammalian species while variation both in terms of length of Dicer enzyme and presence or absence of domain is evident in the lower organisms.

• Journal of the Korean Society of Earth Science Education
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• v.12 no.1
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• pp.40-53
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• 2019

This study was to analyze the inquiry characteristics of preservice teachers as they classify trilobites. For the study, 70 preservice teachers attending teacher training university participated. The classification tasks used in the study were 9 photos of trilobite fossils. The preservice teachers' inquiry activity was to classify the evolutionary processes of trilobites after observing trilobite fossils by group and then to construct a phylogenetic tree. The results of the study are as follows. First, preservice teachers observed the external features of the trilobites and constructed systematic classification results based on their observed contents. Second, preservice teachers classified trilobites using various classification criteria. Third, the phylogenetic tree of preservice teachers and the phylogenetic tree of scientists were very similar. The preservice teachers constructed a sphylogenetic tree based on the observation and inference of the change from a simple form to a complex form, which is a general evolution process of the trilobite fossil claimed by scientists. These results suggest that group-based inquiry activities with sufficient time are very effective and that the experience of inquiry activities is very important for preservice teachers.