TTo address the computational demands of biochemical parameter estimation, we combine PPSO with GPU-accelerated ODE simulation and demonstrate that PPSO outperforms standard PSO in runtime while achieving similar parameter-fitness quality.

Recommended citation: Nobile M.S., Tangherloni A. Besozzi D., Cazzaniga P. (2016). GPU-powered and settings-free parameter estimation of biochemical systems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
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GPU-powered bat algorithm for the parameter estimation of biochemical kinetic values

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2016

A Lévy-flight Bat Algorithm combined with GPU simulation outperforms PSO in accuracy and speed for biochemical parameter estimation.

Recommended citation: Tangherloni A., Nobile M.S., Cazzaniga P. (2016). GPU-powered bat algorithm for the parameter estimation of biochemical kinetic values. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems

Published in IEEE Congress on Evolutionary Computation (CEC), 2017

To address PSO’s sensitivity to parameter settings, we propose PPSO, a fuzzy logic–driven algorithm that proactively adapts parameters for each particle and improves convergence and performance across high-dimensional benchmarks.

Recommended citation: Tangherloni A., Rundo L., Nobile M.S. (2017). Proactive Particles in Swarm Optimization: A settings-free algorithm for real-parameter single objective optimization problems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
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Computational Intelligence for Parameter Estimation of Biochemical Systems

Published in IEEE Congress on Evolutionary Computation (CEC), 2018

To address the challenge of estimating kinetic parameters in biochemical models, we benchmark seven state-of-the-art optimization techniques and find that a settings-free FST-PSO variant yields the most robust and accurate performance across diverse systems.

Recommended citation: Nobile M.S., Tangherloni A., Rundo L., Spolaor S., Besozzi D., Mauri G., Cazzaniga P. (2018). Computational Intelligence for Parameter Estimation of Biochemical Systems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
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The impact of representation on the optimization of marker panels for single-cell RNA data

Published in IEEE Congress on Evolutionary Computation (CEC), 2021

To address the NP-hard challenge of marker panel identification, we introduce and compare three GA representations, demonstrating that more flexible encodings yield the most effective panels, especially in 0-knowledge scenarios.

Recommended citation: Tangherloni A., Riva S.G., Spolaor S., Besozzi D., Nobile M.S., Cazzaniga P. (2021). The impact of representation on the optimization of marker panels for single-cell RNA data. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
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A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2022

scALPO is a novel automated pipeline that employs an LSTM neural network to assign cell types based solely on marker genes, surpassing current state-of-the-art tools in annotation accuracy.

Recommended citation: Riva S.G., Myers B., Cazzaniga P., Tangherloni A.(2022).A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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Multi-objective Optimization for Marker Panel Identification in Single-cell Data

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2022

To address the complexity of selecting minimal yet discriminative marker panels, we model the task as a bi-objective optimization problem and evaluate multi-objective algorithms that outperform genetic algorithms in both quality and consistency.

Recommended citation: Tangherloni A., Riva S.G., Myers B., Cazzaniga P. (2022). Multi-objective Optimization for Marker Panel Identification in Single-cell Data. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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The Domination Game: Dilating Bubbles to Fill Up Pareto Fronts

Published in IEEE Congress on Evolutionary Computation (CEC), 2023

By manipulating the search space via LBDFs, our method reliably discovers new non-dominated solutions and improves Pareto front quality across standard multi-objective benchmarks.

Recommended citation: Coelho V., Papetti D.M., Tangherloni A., Cazzaniga P., Besozzi D., Nobile M.S. (2023). The Domination Game: Dilating Bubbles to Fill Up Pareto Fronts. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
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Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2023

We introduce a pseudo-voting consensus approach within scALPO that combines multiple clustering outputs to achieve more robust and accurate cell-type annotation.

Recommended citation: Riva S.G., Myers B., Cazzaniga P., Buffa F.M., Tangherloni A. (2023). Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq Data. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems

Published in IEEE Congress on Evolutionary Computation (CEC), 2024

We introduce iEACOP, an enhanced evolutionary algorithm with minimal hyper-parameter tuning requirements, which outperforms the original EACOP on most CEC 2017 benchmark functions and achieves performance comparable to top competition algorithms.

Recommended citation: Tangherloni A., Coelho V., Buffa F.M., Cazzaniga P. (2024). A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems. In IEEE Congress on Evolutionary Computation (CEC), IEEE.
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A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2024

We propose a novel and efficient feature-selection strategy for Fuzzy Inference Systems that uses Random Forest–based variable ranking to identify the smallest set of informative features.

Recommended citation: Tangherloni A., Cazzaniga P., Stranieri N., Buffa F.M., Nobile M.S. (2024). A Fast Feature Selection for Interpretable Modeling Based on Fuzzy Inference Systems. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2024

We introduce FP, a novel forest-structured evolutionary algorithm that models complex gene regulatory interactions and reconstructs Boolean GRNs with efficient convergence and strong precision.

Recommended citation: Stranieri N., Buffa F.M.,Tangherloni A. (2024). Forest-based Evolutionary Algorithm for Reconstructing Boolean Gene Regulatory Networks. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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We Are Sending You Back… to the Optimum! Fuzzy Time Travel Particle Swarm Optimization

Published in International Conference on the Applications of Evolutionary Computation (Part of EvoStar), 2025

To prevent FST-PSO from getting trapped in local optima, we propose FTT-PSO, a time-travel strategy that rewinds the swarm and reinitialises the global best when progress stalls, yielding superior results on major benchmark suites.

Recommended citation: Papetti D.M., Tangherloni A., Coelho V., Besozzi D., Cazzaniga P., Nobile M.S. (2025). We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization. In International Conference on the Applications of Evolutionary Computation (Part of EvoStar), Springer.
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GENESIS: Generating scRNA-Seq data from Multiome Gene Expression

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2025

We introduce GENESIS, a generative modelling framework that bridges the gap between Multiome GEX and scRNA-Seq data by producing enhanced whole-cell–like expression profiles.

Recommended citation: Riva S.G., Myers B., Buffa F.M., Tangherloni A. (2025). GENESIS: Generating scRNA-Seq data from Multiome Gene Expression. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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Knowledge-Enriched Cell-Type Annotation in Single-Cell Transcriptomics via LLM Embeddings

Published in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2025

We show that embedding biological knowledge through Modern-BERT gene vectors strengthens supervised models for scRNA-seq cell-type classification.

Recommended citation: Fabbricatore A., Buffa F.M., Tangherloni A. (2025). Knowledge-Enriched Cell-Type Annotation in Single-Cell Transcriptomics via LLM Embeddings. In IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), IEEE.
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GPU-powered and settings-free parameter estimation of biochemical systems