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Author

H. Ishibuchi

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Oct 2026

Performance Comparison of Constrained Multi-objective Evolutionary Algorithms on Artificial and Real-world Benchmarking Problems

Real-world multi-objective optimization problems usually have multiple constraints. To solve constrained multi-objective optimization problems (CMOPs), researchers have proposed various constrained multi-objective evolutionary algorithms (CMOEAs). These CMOEAs explicitly or implicitly assume that there are large infeas...

Yang Nan, H. Ishibuchi, Lie-Meng Pang · 0 citations
#machine learning Open access Sep 2026

Kolmogorov-Arnold Classifier Systems as Universal Approximators

The proposed Kolmogorov-Arnold Classifier System (KACS) decomposes the target function into one-dimensional subproblems and assigns a dedicated ruleset to each, reducing the worst-case rule count from $\mathcal{O}(m^n)$ to $\mathcal{O}(mn^2)$ and replacing $n$-dimensional local models with one-dimensional models requir...

Hiroki Shiraishi, H. Ishibuchi, Masaya Nakata · 0 citations

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