Genetic Algorithms

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9 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasets
May 13, 2026Amine Boukhari, Boglarka Ecsedi, Laszlo Papp +1Deep Learning Architectures Gradient Descent
Laboratory of medical information processing (LaTIM), INSERM, UMR 1101,university of western brittany · Center for Medical Physics and Biomedical Engineering, Medical University of Vienna · Georgia Institute of Technology
A Comparative Study of Model Selection Criteria for Symbolic Regression
May 11, 2026Ali Soltani, Gabriel Kronberger, Fabricio Olivetti de Franca +2Symbolic Regression Model Selection
Aarhus University Department of Mechanical Engineering Aarhus, Denmark · Heuristic and Evolutionary Algorithms Laboratory (HEAL) University of Applied Sciences Upper Austria Hagenberg, Austria · Federal University of ABC Center for Mathematics, Computing, and Cognition Brazil +1
GESR: A Genetic Programming-Based Symbolic Regression Method with Gene Editing
May 11, 2026Yanjie Li, Liping Zhang, Min Wu +6Symbolic Regression Genetic Algorithms
AnnLab, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China. · School of Electronic, Electrical and Communication Engineering,2026 University of Chinese Academy of Sciences, Beijing, China.
Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
May 5, 2026Job Groeneveld, Miguel Muñoz, Jan Viebahn +1Optimal Power Flow Power Systems
TenneT TSO B.V., Arnhem, The Netherlands · Artelys, Paris, France · Department of Mathematics, Vrije Universiteit Amsterdam, The Netherlands
Neuromorphic Control for 3D Navigation in Minecraft Using Genetic Algorithms
May 4, 2026Eric ZiporPath Planning Maze Task
Department of Electrical and Computer Engineering George Mason University, Fairfax, VA, USA
Relation Reasoning with LLMs in Expensive Optimization
Apr 30, 2026Ye Lu, Bingdong Li, Aimin Zhou +1Optimization Modeling Large Language Model Reinforcement Learning
Shanghai Institute of Al for Education, East China Normal University, Shanghai 200062, China
EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming
Apr 28, 2026Xuanhao Yang, Bing Xue, Mengjie ZhangTime-Series Classification Genetic Algorithms
Centre for Data Science and Artificial Intelligence & School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington 6140, New Zealand
Multi-action Tangled Program Graphs for Multi-task Reinforcement Learning with Continuous Control
Apr 28, 2026Quentin Vacher, Nicolas Beuve, Mickaël Dardaillon +1Multi-Turn Reinforcement Learning Genetic Algorithms
IETR · Univ Rennes, INSA Rennes, CNRS, IETR – UMR 6164, F-35000 Rennes, France
The Effects of Population Size on the Performance of BEAGLE GPU-Based Genetic Programming Runs
Apr 27, 2026Nathan Haut, Ilya Basin, Ruchika Gupta +4Genetic Algorithms Symbolic Regression
Michigan State University, East Lansing MI 48824, USA · Noblis, Reston VA , USA
Differentiable Satellite Constellation Configuration via Relaxed Coverage and Revisit Objectives
Apr 21, 2026Shreeyam Kacker, Kerri CahoyMultiple Satellite Systems Near-Earth Objects
Department of Aeronautics and Astronautics MIT
On Scalability of Multi-Objective Evolutionary Algorithms on Combinatorial Optimisation Problems
Apr 20, 2026Menghao Tang, Zimin Liang, Miqing LiMulti-Objective Optimization Combinatorial Optimization
University of Birmingham Birmingham, United Kingdom
On the Generalization Bounds of Symbolic Regression with Genetic Programming
Apr 19, 2026Masahiro Nomura, Ryoki Hamano, Isao OnoSymbolic Regression Generalization Bounds
Institute of Science Tokyo, Yokohama, Japan · CyberAgent, Shibuya, Japan
Monotone but Exciting: On Evolving Monotone Boolean Functions with High Nonlinearity
Apr 19, 2026Claude Carlet, Marko Čupić, Marko Ðurasevic +3Monotonicity Genetic Algorithms
University of Paris 8, 2 rue de la liberté, 93526 Saint-Denis Cedex, France · University of Bergen, Bergen, Norway · Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, Zagreb, Croatia +2
Evolutionary design of thermodynamic logic gates and their heat emission
Feb 13, 2026Stephen WhitelamThermodynamics Genetic Algorithms
Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Information entropy as an anthropomorphic concept
Mar 6, 2015Panteleimon RodisEntropy Thermodynamics
Diploma in Computer Science, Hellenic Open University
Design of statistical quality control procedures using genetic algorithms
Jan 27, 2002Aristides T. Hatjimihail, Theophanes T. HatjimihailGenetic Algorithms
Hellenic Complex Systems Laboratory, Drama, Greece · Hellenic Complex Systems Laboratory, P.O. Box 56, GR-66100 Drama, Greece

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Genetic algorithm vs. gradient descent for training a neural network architecture dedicated to low data regimes in small medical datasets

Amine Boukhari, Boglarka Ecsedi, Laszlo Papp, Mathieu Hatt

Laboratory of medical information processing (LaTIM), INSERM, UMR 1101,university of western brittany · Center for Medical Physics and Biomedical Engineering, Medical University of Vienna · Georgia Institute of Technology

Deep Learning Architectures Gradient Descent Deep Learning Genetic Algorithms Spatial Encoding

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Aim/Introduction: Distance-encoding biomorphic-informational neural network (DEBI-NN) is a recently proposed architecture in which connection weights are defined by the distances between neurons positioned in a Euclidian space. This approach drastically reduces the number of trainable parameters compared to classical neural networks in which weights are directly trained. The training process for DEBI-NN is based on a genetic algorithm (GA), rather than gradient descent (GD) which remains the prevailing optimization algorithm in deep learning. We aim to design and implement a GD learner for DEBI-NN and assess its performance compared to GA. Materials and Methods: We designed a spatial backpropagation scheme tailored to DEBI-NN and carried out a comparison between GD and GA for classification tasks, using a synthetic non-linear "two-moons" dataset, two clinical medical imaging radiomic datasets and a fetal cardiotocography dataset with a sample sizes ranging from n=85 to n=2126. Each optimizer was tuned through targeted hyperparameter searches adapted to each dataset. Results: Across all experiments, GA consistently produced superior decision boundaries and classification performance (Synthetic: 100% vs 83%; DLBCL: 83% vs 78%; HECKTOR: 80% vs 67%; Fetal: 81% vs 66%), whereas GD exhibited instability and failed to fully capture the non-linear patterns inherent to DEBI-NN's spatial encoding. The entangled gradients resulting from neuron interdependencies limit the effectiveness of classical backpropagation. Conclusion: These findings highlight fundamental limitations of gradient-based methods in architectures with highly interdependent spatial parameters and confirm the suitability of evolutionary strategies for training DEBI-NN.

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