Daiki Morinaga

16 papers A 4Journal 10Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	CMA-ES with Individual Adaptive Reevaluation for Black-Box Value-At-Risk Optimization. GECCO Companion Ze Kai Ng, Daiki Morinaga, Youhei Akimoto
2025	A	`conf`	Challenges of Interaction in Optimizing Mixed Categorical-Continuous Variables. GECCO Youhei Akimoto, Xilin Gao, Ze Kai Ng, Daiki Morinaga
2025	J	`jnl`	Challenges of Interaction in Optimizing Mixed Categorical-Continuous Variables. CoRR Youhei Akimoto, Xilin Gao, Ze Kai Ng, Daiki Morinaga
2025	J	`jnl`	Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm. Evol. Comput. Ryoki Hamano, Kento Uchida, Shinichi Shirakawa, Daiki Morinaga, Youhei Akimoto
2025	J	`jnl`	Theoretical Analysis of Explicit Averaging and Novel Sign Averaging in Comparison-Based Search. IEEE Trans. Evol. Comput. Daiki Morinaga, Youhei Akimoto
2024	J	`jnl`	Convergence Rate of the (1+1)-ES on Locally Strongly Convex and Lipschitz Smooth Functions. IEEE Trans. Evol. Comput. Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
2024	—	`conf`	Linear Convergence Rate Analysis of the (1+1)-ES on Locally Strongly Convex and Lipschitz Smooth Functions. GECCO Companion Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
2024	A	`conf`	Sign-Averaging Covariance Matrix Adaptation Evolution Strategy. GECCO Daiki Morinaga, Youhei Akimoto
2024	J	`jnl`	Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm. CoRR Ryoki Hamano, Kento Uchida, Shinichi Shirakawa, Daiki Morinaga, Youhei Akimoto
2024	J	`jnl`	Theoretical Analysis of Explicit Averaging and Novel Sign Averaging in Comparison-Based Search. CoRR Daiki Morinaga, Youhei Akimoto
2022	J	`jnl`	Convergence rate of the (1+1)-evolution strategy on locally strongly convex functions with lipschitz continuous gradient and their monotonic transformations. CoRR Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
2022	J	`jnl`	Global Linear Convergence of Evolution Strategies on More than Smooth Strongly Convex Functions. SIAM J. Optim. Youhei Akimoto, Anne Auger, Tobias Glasmachers, Daiki Morinaga
2021	J	`jnl`	Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions. CoRR Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
2021	A	`conf`	Convergence rate of the (1+1)-evolution strategy with success-based step-size adaptation on convex quadratic functions. GECCO Daiki Morinaga, Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto
2020	J	`jnl`	Global Linear Convergence of Evolution Strategies on More Than Smooth Strongly Convex Functions. CoRR Youhei Akimoto, Anne Auger, Tobias Glasmachers, Daiki Morinaga
2019	A	`conf`	Generalized drift analysis in continuous domain: linear convergence of (1 + 1)-ES on strongly convex functions with Lipschitz continuous gradients. FOGA Daiki Morinaga, Youhei Akimoto

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.