Tae Jong Choi

Chonnam National University

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Trends

Author Order

Document Type

Co-Authors

Chang Wook Ahn

Gwangju Institute of Science and Technology

Julian Togelius

New York University

Yun-Gyung Cheong

Sungkyunkwan University

Ba Hung Ngo

Dongguk University

Vinh Quang Dinh

Vietnamese-German University

Doanh C. Bui

Vietnam National University Ho Chi Minh City

Nhat-Tuong Do-Tran

National Yang Ming Chiao Tung University

Nikhil Pachauri

Manipal Academy of Higher Education

Hae‐Gon Jeon

International Graduate School of English

Yeon-Ju Lee

Korea University

Cooperative Institutions

Sungkyunkwan University

Gwangju Institute of Science and Technology

New York University

Samsung (South Korea)

Korea University

Korea Advanced Institute of Science and Technology

Vietnam National University Ho Chi Minh City

Kyungpook National University

Konkuk University

Ho Chi Minh City University of Science

Author Statistics

Papers

Citation

H-Index

i-10 index

Research Field

A Fast and efficient stochastic opposition-based learning for differential evolution in numerical optimization

Swarm and Evolutionary Computation (2020)

Tae Jong Choi Julian Togelius Yun-Gyung Cheong

Differential Evolution

10.1016/j.swevo.2020.100768

Cite

Citations (31)

Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization

arXiv (Cornell University) (2019)

Tae Jong Choi Julian Togelius Yun-Gyung Cheong

Among many evolutionary algorithms, differential evolution (DE) has received much attention over the last two decades. DE is a simple yet powerful evolutionary algorithm that has been used successfully to optimize various real-world problems. Since it was introduced, many researchers have developed new methods for DE, and one of them makes use of a mutation based on the Cauchy distribution to increase the convergence speed of DE. The method monitors the results of each individual in the selection operator and performs the Cauchy mutation on consecutively failed individuals, which generates mutant vectors by perturbing the best individual with the Cauchy distribution. Therefore, the method can locate the consecutively failed individuals to new positions close to the best individual. Although this approach is interesting, it fails to take into account establishing a balance between exploration and exploitation. In this paper, we propose a sigmoid based parameter control that alters the failure threshold for performing the Cauchy mutation in a time-varying schedule, which can establish a good ratio between exploration and exploitation. Experiments and comparisons have been done with six conventional and six advanced DE variants on a set of 30 benchmark problems, which indicate that the DE variants assisted by the proposed algorithm are highly competitive, especially for multimodal functions.

Differential Evolution

Benchmark (surveying)

Sigmoid function

Source

Cite

Citations (0)

Autoencoder and Evolutionary Algorithm for Level Generation in Lode Runner

2021 IEEE Conference on Games (CoG) (2019)

Sarjak Thakkar Changxing Cao Lifan Wang Tae Jong Choi Julian Togelius

Procedural content generation can be used to create arbitrarily large amounts of game levels automatically, but traditionally the PCG algorithms needed to be developed or adapted for each game manually. Procedural Content Generation via Machine Learning (PCGML) harnesses the power of machine learning to semi-automate the development of PCG solutions, training on existing game content so as to create new content from the trained models. One of the machine learning techniques that have been suggested for this purpose is the autoencoder. However, very limited work has been done to explore the potential of autoencoders for PCGML. In this paper, we train autoencoders on levels for the platform game Lode Runner, and use them to generate levels. Compared to previous work, we use a multi-channel approach to represent content in full fidelity, and we compare standard and variational autoencoders. We also evolve the values of the hidden layer of trained autoencoders in order to find levels with desired properties.

Autoencoder

Lode

Content (measure theory)

10.1109/cig.2019.8848076

Cite

Citations (39)

Learning CNN on ViT: A Hybrid Model to Explicitly Class-Specific Boundaries for Domain Adaptation

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

Ba Hung Ngo Nhat-Tuong Do-Tran Tuan-Ngoc Nguyen Hae‐Gon Jeon Tae Jong Choi

Domain Adaptation

10.1109/cvpr52733.2024.02697

Cite

Citations (0)

An Adaptive Cauchy Differential Evolution Algorithm with Population Size Reduction and Modified Multiple Mutation Strategies

Proceedings in adaptation, learning and optimization (2014)

Tae Jong Choi Chang Wook Ahn

Benchmark (surveying)

Differential Evolution

10.1007/978-3-319-13356-0_2

Cite

Citations (17)

A Genetic Algorithm with Location Intelligence Method for Energy Optimization in 5G Wireless Networks

Discrete Dynamics in Nature and Society (2016)

Ruchi Sachan Tae Jong Choi Chang Wook Ahn

The exponential growth in data traffic due to the modernization of smart devices has resulted in the need for a high-capacity wireless network in the future. To successfully deploy 5G network, it must be capable of handling the growth in the data traffic. The increasing amount of traffic volume puts excessive stress on the important factors of the resource allocation methods such as scalability and throughput. In this paper, we define a network planning as an optimization problem with the decision variables such as transmission power and transmitter (BS) location in 5G networks. The decision variables lent themselves to interesting implementation using several heuristic approaches, such as differential evolution (DE) algorithm and Real-coded Genetic Algorithm (RGA). The key contribution of this paper is that we modified RGA-based method to find the optimal configuration of BSs not only by just offering an optimal coverage of underutilized BSs but also by optimizing the amounts of power consumption. A comparison is also carried out to evaluate the performance of the conventional approach of DE and standard RGA with our modified RGA approach. The experimental results showed that our modified RGA can find the optimal configuration of 5G/LTE network planning problems, which is better performed than DE and standard RGA.

Differential Evolution

10.1155/2016/5348203

Cite

Citations (28)

An Improved LSHADE-RSP Algorithm with the Cauchy Perturbation: iLSHADE-RSP

arXiv (Cornell University) (2020)

Tae Jong Choi Chang Wook Ahn

A new method for improving the optimization performance of a state-of-the-art differential evolution (DE) variant is proposed in this paper. The technique can increase the exploration by adopting the long-tailed property of the Cauchy distribution, which helps the algorithm to generate a trial vector with great diversity. Compared to the previous approaches, the proposed approach perturbs a target vector instead of a mutant vector based on a jumping rate. We applied the proposed approach to LSHADE-RSP ranked second place in the CEC 2018 competition on single objective real-valued optimization. A set of 30 different and difficult optimization problems is used to evaluate the optimization performance of the improved LSHADE-RSP. Our experimental results verify that the improved LSHADE-RSP significantly outperformed not only its predecessor LSHADE-RSP but also several cutting-edge DE variants in terms of convergence speed and solution accuracy.

Differential Evolution

Source

Cite

Citations (0)

Self-Referential Quality Diversity Through Differential Map-Elites

arXiv (Cornell University) (2021)

Tae Jong Choi Julian Togelius

Differential MAP-Elites is a novel algorithm that combines the illumination capacity of CVT-MAP-Elites with the continuous-space optimization capacity of Differential Evolution. The algorithm is motivated by observations that illumination algorithms, and quality-diversity algorithms in general, offer qualitatively new capabilities and applications for evolutionary computation yet are in their original versions relatively unsophisticated optimizers. The basic Differential MAP-Elites algorithm, introduced for the first time here, is relatively simple in that it simply combines the operators from Differential Evolution with the map structure of CVT-MAP-Elites. Experiments based on 25 numerical optimization problems suggest that Differential MAP-Elites clearly outperforms CVT-MAP-Elites, finding better-quality and more diverse solutions.

Differential Evolution

Source

Cite

Citations (1)

An Adaptive Population Resizing Scheme for Differential Evolution in Numerical Optimization

Journal of Computational and Theoretical Nanoscience (2015)

Tae Jong Choi Chang Wook Ahn

Resizing

Differential Evolution

10.1166/jctn.2015.3895

Cite

Citations (11)

Learning CNN on ViT: A Hybrid Model to Explicitly Class-specific Boundaries for Domain Adaptation

arXiv (Cornell University) (2024)

Ba Hung Ngo Nhat-Tuong Do-Tran Tuan-Ngoc Nguyen Hae‐Gon Jeon Tae Jong Choi

Most domain adaptation (DA) methods are based on either a convolutional neural networks (CNNs) or a vision transformers (ViTs). They align the distribution differences between domains as encoders without considering their unique characteristics. For instance, ViT excels in accuracy due to its superior ability to capture global representations, while CNN has an advantage in capturing local representations. This fact has led us to design a hybrid method to fully take advantage of both ViT and CNN, called Explicitly Class-specific Boundaries (ECB). ECB learns CNN on ViT to combine their distinct strengths. In particular, we leverage ViT's properties to explicitly find class-specific decision boundaries by maximizing the discrepancy between the outputs of the two classifiers to detect target samples far from the source support. In contrast, the CNN encoder clusters target features based on the previously defined class-specific boundaries by minimizing the discrepancy between the probabilities of the two classifiers. Finally, ViT and CNN mutually exchange knowledge to improve the quality of pseudo labels and reduce the knowledge discrepancies of these models. Compared to conventional DA methods, our ECB achieves superior performance, which verifies its effectiveness in this hybrid model. The project website can be found https://dotrannhattuong.github.io/ECB/website/.

Domain Adaptation

10.48550/arxiv.2403.18360

Cite

Citations (0)