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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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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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cartesian genetic programming as an Optimizer of Programs Evolved with Geometric Semantic genetic programming 22nd

Cartesian Genetic Programming as an Optimizer of Programs Ev...

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22nd European Conference on genetic programming (EuroGP) Held as Part of EvoStar Conference

作者： Koncal, Ondrej Sekanina, Lukas Brno Univ Technol IT4Innovat Ctr Excellence Fac Informat Technol Bozetechova 2 Brno 61266 Czech Republic

ISBN: (纸本)9783030166694;9783030166700

In Geometric Semantic genetic programming (GSGP), genetic operators directly work at the level of semantics rather than syntax. It provides many advantages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree cartesian genetic programming (SCGP) - a method capable of reducing the number of nodes in the trees generated by GSGP. SCGP executes a common cartesian genetic programming (CGP) on all elementary subtrees created by GSGP and on various compositions of these optimized subtrees in order to create one compact representation of the original program. SCGP does not guarantee the (exact) semantic equivalence between the CGP individuals and the GSGP subtrees, but the user can define conditions when a particular CGP individual is acceptable. We evaluated SCGP on four common symbolic regression benchmark problems and the obtained node reduction is from 92.4% to 99.9%.

关键词： cartesian genetic programming Geometric Semantic genetic programming Symbolic regression

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cartesian genetic programming Is Robust Against Redundant Attributes in Datasets 16th

Cartesian Genetic Programming Is Robust Against Redundant At...

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16th International Joint Conference on Computational Intelligence, IJCCI 2024

作者： Cui, Henning Hähner, Jörg University of Augsburg Augsburg86159 Germany

ISBN: (纸本)9789897587214

Real world datasets might contain duplicate or redundant attributes—or even pure noise—which may not be filtered out by data preprocessing algorithms. This might be problematic, as it decreases the performance of learning algorithms. cartesian genetic programming (CGP) is able to choose its own input attributes by design. Thus, we hypothesize that CGP should be able to ignore redundant or noise attributes. In this work, we empirically show that CGP is indeed able to handle such problematic datasets. For this task, six different datasets are extended with different kinds of redundancies: Duplicated-, duplicated and noised-, and pure noise attributes. Different numbers of unwanted attributes are examined, and we present our results which indicate that CGP is robust against additional redundant or noisy attributes in a dataset. We show that there is no decrease in performance as well as no change in CGP’s convergence behaviour. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： cartesian genetic programming CGP Duplicate Attributes Noisy Attributes

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Developments in cartesian genetic programming: self-modifying CGP

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genetic programming AND EVOLVABLE MACHINES 2010年第3-4期11卷 397-439页

作者： Harding, Simon Miller, Julian F. Banzhaf, Wolfgang Mem Univ Newfoundland Dept Comp Sci St John NF A1B 3X5 Canada Univ York Dept Elect York YO10 5DD N Yorkshire England

Self-modifying cartesian genetic programming (SMCGP) is a general purpose, graph-based, developmental form of genetic programming founded on cartesian genetic programming. In addition to the usual computational functions, it includes functions that can modify the program encoded in the genotype. This means that programs can be iterated to produce an infinite sequence of programs (phenotypes) from a single evolved genotype. It also allows programs to acquire more inputs and produce more outputs during this iteration. We discuss how SMCGP can be used and the results obtained in several different problem domains, including digital circuits, generation of patterns and sequences, and mathematical problems. We find that SMCGP can efficiently solve all the problems studied. In addition, we prove mathematically that evolved programs can provide general solutions to a number of problems: n-input even-parity, n-input adder, and sequence approximation to pi.

关键词： cartesian genetic programming Developmental systems

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