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Hardware real-time individualised blood glucose predictor generator based on grammars and cartesian genetic programming

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genetic programming AND EVOLVABLE MACHINES 2025年第1期26卷 1-26页

作者： Cano, Jorge Hidalgo, J. Ignacio Garnica, Oscar Univ Complutense Madrid Dept Arquitectura Comp & Automat Calle Prof Jose Garcia Santesmases Madrid 28040 Comunidad De Ma Spain

In this paper, we introduce a novel grammar-guided technique based on genetic programming for on-chip, real-time, configurable hardware design of model generators on an FPGA. The technique integrates grammar-based design, cartesian genetic programming, and a (1+lambda\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\lambda$$\end{document}) Evolutionary Strategy and is demonstrated through the implementation of a wearable hardware predictor for blood glucose prediction. People with diabetes need to manage their blood glucose levels to prevent life-threatening situations and long-term complications. Effective glucose management requires accurate blood glucose predictions, yet most existing methods rely on heuristic estimators. This system enables the training and testing of personalized models using real patient data. We validated the approach by generating and evaluating models for 30- and 60-min forecasting predictions on ten patients, creating a total of 200 models. The system achieved state-of-the-art results, with 98% and 90% of predictions falling within clinically acceptable regions according to Clarke error grid analysis, for 30- and 60-min horizons, respectively. Unlike software implementations, our technique does not suffer from hardware limitations and provides an efficient, adaptable solution through wearable hardware with minimal errors and low power consumption. This is the first demonstration of combining cartesian genetic programming with a hardware implementation for grammar-based blood glucose prediction, potentially enabling real-time embedded systems for portable devices.

关键词： Blood glucose prediction FPGA Edge artificial intelligence cartesian genetic programming Grammatical evolution Model generator

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Ant-Based Metaheuristics Struggle to Solve the cartesian genetic programming Learning Task

Ant-Based Metaheuristics Struggle to Solve the Cartesian G...

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28th European Conference on genetic programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Trautwein, Julian Heider, Michael Cui, Henning Hähner, Jörg University of Augsburg Augsburg 86159 Germany

ISBN: (纸本)9783031899904

Ant-based metaheuristics have successfully been applied to a variety of different graph-based problems. However, for cartesian genetic programming (CGP) only the impact of Max-Min Ant Systems has been tested. In this work, we try to fill this gap by applying four different popular ant-based metaheuristics as the optimizer (and therefore training algorithm) of CGP. The idea of combining CGP with ant-based metaheuristics is not novel but older works’ experimental design may not meet today’s standard. To compare these metaheuristics to the Evolution Strategies (ESs) commonly used in CGP, we benchmark against a standard CGP variant that uses a simplistic -ES, mutation, and no crossover. Additionally, we include -ES and -ES in our experiments. We analyse the performance on datasets from the symbolic regression, regression, and classification domains. By tuning and evaluating various configurations, we can not affirm a significant improvement by using ant-based methods with CGP as we encounter premature convergence—even with those ant-based metaheuristics that were originally proposed to overcome such problems. Despite our results being of negative nature, this work still gives important and interesting insights into the training of CGP models. The key contributions of our work are thus a more thorough benchmarking of these optimizers than has been done before. This should clear up doubts about the capabilities of ant-based metaheuristics in CGP. Furthermore, we include a roadmap on how they can be addressed to solve this complex optimization problem from the model building domain of machine learning. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ant Colony Optimization Ant-based Metaheuristic cartesian genetic programming Evolution Strategies Evolutionary Algorithm

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Designing Hardware-Friendly Hash Functions for Network Security Using cartesian genetic programming

Designing Hardware-Friendly Hash Functions for Network Secu...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Hassan, Mujtaba Vliegen, Jo Picek, Stjepan Mentens, Nele ES&S COSIC ESAT KU Leuven Leuven Belgium Digital Security Group Radboud University Nijmegen Netherlands LIACS Leiden University Leiden Netherlands

ISBN: (纸本)9783031900617

In this study, we propose a novel, hardware-efficient non-cryptographic (NC) hash function, developed using cartesian genetic programming (CGP), to optimize the processing of network flows (e.g., 96-bit vectors in IPv4). With the advent of high-speed terabit Ethernet technologies such as 800G, cybercriminals are increasingly exploiting these networks to launch various attacks, including distributed denial-of-service (DDoS) attacks. To counter these threats, network security applications often employ probabilistic data structures (PDS), such as Bloom filters and Count Min sketches, to monitor network flows. These applications are often deployed on Field Programmable Gate Arrays (FPGAs) to meet real-time processing demands. The performance of PDS depends significantly on the efficiency of NC-hash functions. By utilizing avalanche metrics (avalanche dependence, avalanche weight, and entropy) as the fitness function, CGP-hash ensures robust performance without the need for dataset-specific training. While genetic programming (GP)-based NC-hash functions have demonstrated superior computational efficiency on FPGAs in terms of operating frequency, throughput, and latency, they are often less resource-efficient compared to state-of-the-art NC-hash functions. Thus, our hypothesis in this paper is that CGP, with its compact representation, leads to NC hashes with high computational efficiency and fewer resources on an FPGA. Our experimental results confirm that CGP-hash improves computational efficiency by at least 7.3% while improving area efficiency by 4.5× compared to state-of-the-art NC-hash functions. This makes CGP-hash more suitable for FPGA implementations than other bio-inspired and handcrafted hash functions. Moreover, the 48-bit hash output can be extended by evolving additional hash functions and concatenating their outputs, all while maintaining superior resource efficiency and computational speed. © The Author(s), under exclusive license to Springer Nature Swit

关键词： Bloom filters cartesian genetic programming Network flow detection Non-cryptographic hash function

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On the Effectiveness of Crossover Operators in cartesian genetic programming

On the Effectiveness of Crossover Operators in Cartesian ...

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28th European Conference on genetic programming, EuroGP 2025, Held as Part of EvoStar 2025

作者： Kocherovsky, Mark Kianinejad, Marzieh Bakurov, Illya Banzhaf, Wolfgang Department of Computer Science and BEACON Center for the Study of Evolution in Action Michigan State University East Lansing 48824 MI United States

ISBN: (纸本)9783031899904

This study investigates the effectiveness of cartesian genetic programming by analyzing numerous indicators of evolutionary dynamics when using different crossover operators and the canonical mutation-only (1+4) strategy. Specifically, we examine a traditional crossover operator which is based on the random selection of parental genes;Subgraph Crossover, where points in the range of active nodes are considered;and the recently-proposed Deep Neural Crossover (DNC) approach which utilizes a transformer network to learn correlations between genes and predict potentially beneficial crossover points. The performance of these different crossovers is evaluated on 11 standard and one real-world regression problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： cartesian genetic programming Crossover Crossover Effects Neural Crossover Reinforcement Learning

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cartesian genetic programming: its status and future

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genetic programming AND EVOLVABLE MACHINES 2020年第1-2期21卷 129-168页

作者： Miller, Julian Francis Univ York Dept Elect Engn York N Yorkshire England

cartesian genetic programming, a well-established method of genetic programming, is approximately 20 years old. It represents solutions to computational problems as graphs. Its genetic encoding includes explicitly redundant genes which are well-known to assist in effective evolutionary search. In this article, we review and compare many of the important aspects of the method and findings discussed since its inception. In the process, we make many suggestions for further work which could improve the efficiency of the CGP for solving computational problems.

关键词： cartesian genetic programming genetic programming Evolutionary algorithms

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cartesian genetic programming Parameterization in the Context of Audio Synthesis

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IEEE SIGNAL PROCESSING LETTERS 2023年 30卷 1077-1081页

作者： Ly, Edward Villegas, Julian Univ Aizu Grad Sch Comp Sci & Engn Aizu Wakamatsu Fukushima 9658580 Japan

This letter presents an evaluation of the effects of elitism, recurrence probability, and prior knowledge on the fitness achieved by cartesian genetic programming (CGP) in the context of DSP audio synthesis. Prior knowledge was introduced using a probabilistic learning method where the distribution of nodes in the expected solutions was used to generate and mutate new individuals. Best results were obtained with traditional elitist selection, no recurrence, and when prior knowledge was used for node initialization and mutation. These results suggest that the apparent benefits of recurrence in CGP are context-dependent, and that selecting nodes from a uniform distribution is not always optimal.

关键词： Synthesizers Additives genetic programming Oscillators Feedback loop Computer languages Wheels Audio synthesis cartesian genetic programming digital signal processing evolutionary algorithms

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cartesian genetic programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits 5th

Cartesian Genetic Programming with Guided and Single Active ...

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5th International Conference on Machine Learning, Optimization, and Data Science (LOD)

作者： da Silva, Jose Eduardo H. de Souza, Lucas A. M. Bernardino, Heder S. Univ Fed Juiz de Fora Juiz De Fora MG Brazil

ISBN: (纸本)9783030375997;9783030375980

The design of digital circuits using cartesian genetic programming (CGP) has been widely investigated but the evolution of complex combinational logic circuits is a hard task for CGP. We introduce here a new mutation operator for CGP that aims to reduce the number of evaluations needed to find a feasible solution by modifying the subgraph of the worst output of the candidate circuits. Also, we propose a variant of the standard evolutionary strategy commonly adopted in CGP, where (i) the Single Active Mutation (SAM) and (ii) the proposed mutation operator is used in order to improve the capacity of CGP in generating feasible circuits. The proposals are applied to a benchmark of combinational logic circuits with multiple outputs and the results obtained are compared to those found by a CGP with SAM. The main advantages observed when both mutation operators are combined are the reduction of the number of objective function evaluations required to find a feasible solution and the improvement in the success rate.

关键词： cartesian genetic programming Combinational logic circuit Mutation

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cartesian genetic programming in an Open-Ended Evolution Environment 18th

Cartesian Genetic Programming in an Open-Ended Evolution Env...

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18th EPIA Conference on Artificial Intelligence (EPIA)

作者： Simoes, Antonio Baptista, Tiago Costa, Ernesto Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9783319653402;9783319653396

In this paper we describe and analyze the use of the cartesian genetic programming method to evolve Artificial Neural Networks (CGPANN) in an open-ended evolution scenario. The issue of open-ended evolution has for some time been considered one of the open problems in the field of Artificial Life. In this paper we analyze the capabilities of CGPANN to evolve behaviors in a scenario without artificial selection, more specifically, without the use of explicit fitness functions. We use the BitBang framework and one of its example scenarios as a proof of concept. The results obtained in these first experiments show that it is indeed possible to evolve CGPANN brains, in an open-ended environment, without any explicit fitness function. We also present an analysis of different parameter configurations for the CGPANN when used in this type of scenario.

关键词： Artificial life Multi-agent systems Open-ended evolution Neuroevolution cartesian genetic programming

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cartesian genetic programming for Image Analysis of the Developing Drosophila Eye

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Biophysics (Russian Federation) 2023年第3期68卷 462-467页

作者： Danilov, N.A. Kozlov, K.N. Surkova, S.Y. Samsonova, M.G. Peter the Great St. Petersburg Polytechnic University St. Petersburg 195251 Russian Federation

Abstract: Methods of automatic feature extraction attract increasing attention when solving modern image processing problems. Confocal images of the single-layer epithelium of the developing eye of the fruit fly drosophila are a convenient model system for the development of methods for the identification of complex features. The aim of this study was to use cartesian genetic programming to identify the boundaries of ommatidia, the photosensitive units of the developing eye. The use of cartesian genetic programming to analyze the expression patterns of the Fasciclin III marker showed good results. This provides interesting prospects for further application of this technology for the automatic analysis of images obtained using confocal microscopy. © 2023, Pleiades Publishing, Inc.

关键词： cartesian genetic programming Drosophila eye disc image analysis

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cartesian genetic programming Based Optimization and Prediction

Cartesian Genetic Programming Based Optimization and Predict...

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World Conference on Information Systems and Technologies (WorldCIST)

作者： Seo, Kisung Hyeon, Byeongyong Seokyeong Univ Dept Elect Engn Seoul South Korea

ISBN: (纸本)9783319059518;9783319059501

This paper introduces a CGP (cartesian genetic programming) based optimization and prediction techniques. In order to provide a superior search for optimization and a robust model for prediction, a nonlinear and symbolic regression method using CGP is suggested. CGP uses as genotype a linear string of integers that are mapped to a directed graph. Therefore, some evolved modules for regression polynomials in CGP network can be shared and reused among multiple outputs for prediction of neighborhood precipitation. To investigate the effectiveness of the proposed approach, experiments on gait generation for quadruped robots and prediction of heavy precipitation for local area of Korean Peninsular were executed.

关键词： cartesian genetic programming gait optimization heavy rain prediction symbolic regression

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