Publications

For japanese papers, “(in Japanese)” is written at the end of contents.

All materials on this page are author’s versions, not necessarily coincide with final published versions.
Kei Nishihara, Masaya Nakata (2024). A Surrogate-assisted Partial Optimization for Expensive Constrained Optimization Problems. Proc. Int. Conf. Parallel Probl. Solving Nat. (PPSN), pp. 391–407.

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Tomohiro Harada, Takato Kinoshita, Hiroki Shiraishi, Ryo Takano, Yusuke Tajima, Yuki Tanigaki, Nobuo Namura, Kei Nishihara (2024). Report on Open Space Discussion 2023. Trans. Jpn. Soc. Evol. Comput., Vol. 15, No. 1, pp. 11–19 (in Japanese).

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Yuma Horaguchi, Kei Nishihara, Masaya Nakata (2024). Evolutionary multiobjective optimization assisted by scalarization function approximation for high-dimensional expensive problems. Swarm Evol. Comput., Vol. 86, pp. 101516.

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Kei Nishihara, Masaya Nakata (2024). Emulation-based adaptive differential evolution: fast and auto-tunable approach for moderately expensive optimization problems. Complex & Intell. Syst., Vol. 10, pp. 3633–3656.

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Nobuki Hariya, Kei Nishihara, Masaya Nakata (2023). Symbolic Regression-guided Evolutionary Neural Architecture Search. Proc. Symp. Evol. Comput., pp. 277–284 (in Japanese).

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Kei Nishihara, Masaya Nakata (2023). Surrogate-assisted Evolutionary Algorithm using Pareto-optimal Surrogates Set. Proc. Symp. Evol. Comput., pp. 380–387 (in Japanese).

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Masaya Nakata, Takeshi Uchitane, Junichi Kushida, Shoichiro Tanaka, Yuki Tanigaki, Kei Nishihara, Tomohiro Harada, Yusuke Nojima (2023). Report on Open Space Discussion 2022. Trans. Jpn. Soc. Evol. Comput., Vol. 14, No. 1, pp. 12–17 (in Japanese).

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Nobuki Hariya, Kei Nishihara, Masaya Nakata (2023). Evolutionary Neural Architecture Search Considering Structural Diversity in Deep Learning Models. Proc. Forum Inf. Technol., pp. 79–85 (in Japanese).

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Kei Nishihara, Masaya Nakata (2023). Utilizing the Expected Gradient in Surrogate-assisted Evolutionary Algorithms. Proc. ACM Genet. Evol. Comput. Conf. Companion (GECCO Companion), pp. 447–450.

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Kei Nishihara, Masaya Nakata (2022). Surrogate-assisted Evolutionary Algorithm using Solution Update Performance as a Selection Criterion. Proc. Symp. Evol. Comput., pp. 177–184 (in Japanese).

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Kei Nishihara, Masaya Nakata (2022). Surrogate-assisted Differential Evolution with Adaptation of Training Data Selection Criterion. Proc. IEEE Symp. Ser. Comput. Intell. (SSCI), pp. 1675–1682.

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Yusuke Hiruta, Kei Nishihara, Yuji Koguma, Masakazu Fujii, Masaya Nakata (2022). Automated Construction of Transferable Loading Algorithm with Interactive Genetic Programming. IEEE Access, Vol. 10, pp. 125167–125180.

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Yusuke Nojima, Hideyuki Takagi, Masaharu Munetomo, Naoki Hamada, Kei Nishihara, Keiki Takadama, Hiroyuki Sato, Daiki Kiribuchi, Minami Miyakawa (2022). Report on Open Space Discussion 2021. Trans. Jpn. Soc. Evol. Comput., Vol. 13, No. 1, pp. 1–9 (in Japanese).

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Nobuki Hariya, Kei Nishihara, Masaya Nakata (2021). A Preliminary Investigation of Vision-based Evolutionary Symbolic Regression. Proc. Symp. Evol. Comput., pp. 1–8 (in Japanese).

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Kei Nishihara, Masaya Nakata (2021). High-level Ensemble of Adaptive Differential Evolution with Prior-validation toward Computationally Expensive Optimization Problems. Proc. Symp. Fuzzy Artif. Intell. Neural Netw. Comput. Intell. (FAN), pp. 132–137 (in Japanese).

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Yusuke Hiruta, Kei Nishihara, Yuji Koguma, Masakazu Fujii, Masaya Nakata (2021). Interactive construction of loading algorithm with Genetic Programming. Proc. Annu. Conf. Electron, Inf. Syst., pp. 197–202.

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Kei Nishihara, Masaya Nakata (2021). Performance Improvement with Prior-validation Framework for Algorithmic Configuration on Self-adaptive Differential Evolution. Trans. Math. Model. Appl. (TOM), Vol. 14, No. 3, pp. 51–67 (in Japanese).

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Yusuke Hiruta, Kei Nishihara, Yuji Koguma, Masakazu Fujii, Masaya Nakata (2021). Automated Construction of Transferable Loading Algorithm with Cartesian Genetic Programming. Trans. Math. Model. Appl. (TOM), Vol. 14, No. 3, pp. 11–26 (in Japanese).

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Kei Nishihara, Masaya Nakata (2021). Comparison of Adaptive Differential Evolution Algorithms on the MOEA/D-DE Framework. Proc. IEEE Congr. Evol. Comput. (CEC), pp. 161–168.

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Yushi Miyahara, Takumi Sonoda, Kei Nishihara, Masaya Nakata (2020). Hybrid surrogate-assisted particle swarm optimization based on approximation and classification models. Proc. Symp. Evol. Comput., pp. 227–234 (in Japanese).

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Kei Nishihara, Masaya Nakata (2020). Performance improvement with Prior-validation framework for Algorithmic configuration on Self-adaptive differential evolution. Tech. Rep. Math. Model. Probl. Solving (MPS), Vol. 2020, No. 3, pp. 1–6 (in Japanese).

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Yusuke Hiruta, Kei Nishihara, Yuji Koguma, Masakazu Fujii, Masaya Nakata (2020). Automated construction of Transferable loading algorithm with Cartesian genetic programming. Tech. Rep. Math. Model. Probl. Solving (MPS), Vol. 2020, No. 10, pp. 1–6 (in Japanese).

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Kei Nishihara, Masaya Nakata (2020). Competitive-Adaptive Algorithm-Tuning of Metaheuristics inspired by the Equilibrium Theory: A Case Study. Proc. IEEE Congr. Evol. Comput. (CEC), E-24156, (8).

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Kei Nishihara, Masaya Nakata (2019). Competitively Adaptive Algorithm Tuning inspired by Equilibrium Theory. Proc. Symp. Evol. Comput., pp. 37–44 (in Japanese).

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