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Ensemble Optimization for Invasive Ductal Carcinoma (IDC) Classification Using Differential cartesian genetic programming

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IEEE ACCESS 2022年 10卷 128790-128799页

作者： Alkhaldi, Eid Salari, Ezzatollah Univ Toledo Elect Engn & Comp Sci Dept Toledo OH 43606 USA

The high cost of acquiring annotated histological slides for breast specimens entails exploiting an ensemble of models appropriately trained on small datasets. Histological Image Classification ensembles strive to accurately detect abnormal tissues in the breast samples by determining the correlation between the predictions of its weak learners. Nonetheless, the state-of-the-art ensemble methods, such as boosting and bagging, count merely on manipulating the dataset and lack intelligent ensemble decision making. Furthermore, the methods mentioned above are short of the diversity of the weak models of the ensemble. Likewise, other commonly used voting strategies, such as weighted averaging, are limited to how the classifiers' diversity and accuracy are balanced. Hence, In this paper, we assemble a Neural Network ensemble that integrates the models trained on small datasets by employing biologically-inspired methods. Our procedure is comprised of two stages. First, we train multiple heterogeneous pre-trained models on the benchmark Breast Histopathology Images for Invasive Ductal Carcinoma (IDC) classification dataset. In the second meta-training phase, we utilize the differential cartesian genetic programming (dCGP) to generate a Neural Network that merges the trained models optimally. We compared our empirical outcomes with other state-of-the-art techniques. Our results demonstrate that improvising a Neural Network ensemble using cartesian genetic programming transcended formerly published algorithms on slim datasets.

关键词： cartesian genetic programming hyparameters optimization ensemble CNNs histol-pathaological image classification

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Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design

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genetic programming AND EVOLVABLE MACHINES 2021年第4期22卷 539-572页

作者： Hodan, David Mrazek, Vojtech Vasicek, Zdenek Brno Univ Technol Fac Informat Technol IT4Innovat Ctr Excellence Brno Czech Republic

cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5x5-bit multiplier represents the most complex circuit designed by the evolution from scratch. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in genetic programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. In this paper, we propose a semantically-oriented mutation operator (SOMO k ) suitable for the evolutionary design of combinational circuits. In contrast to standard point mutation modifying the values of the mutated genes randomly, the proposed operator uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10 + 10-bit adder and 5x5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.

关键词： cartesian genetic programming Semantic operator Semantic mutation Evolutionary circuit design

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Cloud Infrastructure Estimation and Auto-Scaling Using Recurrent cartesian genetic programming-Based ANN

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IEEE ACCESS 2020年 8卷 17965-17985页

作者： Ullah, Qazi Zia Khan, Gul Muhammad Hassan, Shahzad Bahria Univ Comp Engn Dept Islamabad 44000 Pakistan COMSATS Univ Islamabad Elect & Comp Engn Dept Attock 43600 Pakistan Peshawar Univ Engn & Technol Elect Engn Dept NCAI Peshawar 25120 Pakistan

Use of cloud resources has increased with the increasing trend of organizations and governments towards cloud adaptation. This increase in cloud resource usage, leads to enormous amounts of energy consumption by cloud data center servers. Energy can be conserved in a cloud server by demand-based scaling of resources. But reactive scaling may lead to excessive scaling. That, in turn, results in enormous energy consumption by useless scale up and scale down. The scaling granularity can also result in excessive scaling of the resource. Without a proper mechanism for estimating cloud resource usage may lead to significant scaling overheads. To overcome, such inefficiencies, we present cartesian genetic programming based neural network for resource estimation and a rule-based scaling system for IaaS cloud server. Our system consists of a resource monitor, a resource estimator and a scaling mechanism. The resource monitor takes resource utilizations and feeds to the estimator for efficient estimation of resources. The scaling system uses the resource estimator's output for scaling the resource with the granularity of a CPU core. The proposed method has been trained and tested with real traces of Bitbrains data center, producing promising results in real-time. It has shown better prediction accuracy and energy efficiency than predictive scaling systems from literature.

关键词： Artificial neural networks auto-scaling cartesian genetic programming energy efficiency evolutionary computation green computing infrastructure as service workload prediction cloud server

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Introducing a cross platform open source cartesian genetic programming library

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genetic programming AND EVOLVABLE MACHINES 2015年第1期16卷 83-91页

作者： Turner, Andrew James Miller, Julian Francis Univ York Dept Elect Intelligent Syst Grp York YO10 5DD N Yorkshire England

cartesian genetic programming (CGP) is a form of genetic programming which encodes computational structures as generic cyclic/acyclic graphs. This letter introduces a new cross platform CGP library intended for use in teaching, academic research and real world applications. This new CGP library is currently capable of evolving symbolic expressions, Boolean logic circuits and Artificial Neural Networks but can easily be extended to other domains. The CGP library, documentation and tutorials are all available at ***

关键词： cartesian genetic programming Software library NeuroEvolution

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Analysis of cartesian genetic programming's Evolutionary Mechanisms

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2015年第3期19卷 359-373页

作者： Goldman, Brian W. Punch, William F. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Understanding how search operators interact with solution representation is a critical step to improving search. In cartesian genetic programming (CGP), and genetic programming (GP) in general, the complex genotype to phenotype map makes achieving this understanding a challenge. By examining aspects such as tuned parameter values, the search quality of CGP variants at different problem difficulties, node behavior, and offspring replacement properties we seek to better understand the characteristics of CGP search. Our focus is two-fold: creating methods to prevent wasted CGP evaluations (skip, accumulate, and single) and creating methods to overcome CGPs search limitations imposed by genome ordering (reorder and DAG). Our results on Boolean problems show that CGP evolves genomes that are highly inactive, very redundant, and full of seemingly useless constants. On some tested problems we found that less than 1% of the genome was actually required to encode the evolved solution. Furthermore, traditional CGP ordering results in large portions of the genome that are never used by any ancestor of the evolved solution. Reorder and DAG allow evolution to utilize the entire genome. More generally, our results suggest that skip-reorder and single-reorder are most likely to solve hard problems using the least number of evaluations and the least amount of time while better avoiding degenerate behavior.

关键词： Analysis cartesian genetic programming

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Using cartesian genetic programming to design wire antenna

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第4期43卷 372-377页

作者： Yu, Zhangyi Zeng, Sanyou China Univ Geosciences Sch Comp Sci Wuhan 430074 Peoples R China

This paper presents a new method which uses cartesian genetic programming (CGP) in order to design wire antenna. Since Julian F. Miller proposed the method of CGP, the research and development of CGP mainly trends in the design of the circuit application in recent years;very few scholars have the related research of wire antenna design in this field. Therefore, the most important feature in this paper is that this is the first time to apply CGP which is originally used for circuit design to make wire antenna design. By numerical test experiments and comparison, we find that this method of wire antenna design is novel and the designed wire antenna also can meet the requirements. This method has the comparative advantages and it is intelligent (self-adaptive, self-organising, self-learning, self-healing, etc.) while it can greatly increase the system speed.

关键词： cartesian genetic programming CGP evolutionary algorithm wire antenna

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FMCGP: frameshift mutation cartesian genetic programming

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COMPLEX & INTELLIGENT SYSTEMS 2021年第3期7卷 1195-1206页

作者： Fang, Wei Gu, Mindan Jiangnan Univ Dept Comp Sci & Technol Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi Jiangsu Peoples R China

cartesian genetic programming (CGP) is a variant of genetic programming (GP) with the individuals represented by a two-dimensional acyclic directed graph, which can flexibly encode many computing structures. In general, CGP only uses a point mutation operator and the genotype of an individual is of fixed size, which may lead to the lack of population diversity and then cause the premature convergence. To address this problem in CGP, we propose a Frameshift Mutation cartesian genetic programming (FMCGP), which is inspired by the DNA mutation mechanism in biology and the frameshift mutation caused by insertion or deletion of nodes is introduced to CGP. The individual in FMCGP has variable-length genotype and the proposed frameshift mutation operator helps to generate more diverse offspring individuals by changing the compiling framework of genotype. FMCGP is evaluated on the symbolic regression problems and Even-parity problems. Experimental results show that FMCGP does not exhibit the bloat problem and the use of frameshift mutation improves the search performance of the standard CGP.

关键词： cartesian genetic programming Evolutionary computation Frameshift mutation

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Unlocking approximation for in-memory computing with cartesian genetic programming and computer algebra for arithmetic circuits

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IT-INFORMATION TECHNOLOGY 2022年第3期64卷 99-107页

作者： Froehlich, Saman Drechsler, Rolf Univ Bremen Arbeitsgrp Rechnerarchitektur D-28359 Bremen Germany DFKI GmbH Cyber Phys Syst D-28359 Bremen Germany

With ReRAM being a non-volative memory technology, which features low power consumption, high scalability and allows for in-memory computing, it is a promising candidate for future computer architectures. Approximate computing is a design paradigm, which aims at reducing the complexity of hardware by trading off accuracy for area and/or delay. In this article, we introduce approximate computing techniques to in-memory computing. We extend existing compilation techniques for the Programmable Logic in-Memory (PLiM) computer architecture, by adapting state-of-the-art approximate computing techniques for arithmetic circuits. We use cartesian genetic programming for the generation of approximate circuits and evaluate them using a Symbolic Computer Algebra-based technique with respect to error-metrics. In our experiments, we show that we can outperform state-of-the-art handcrafted approximate adder designs.

关键词： Approximate Computing In-Memory Computing ReRAM RRAM Symbolic Computer Algebra SCA PLiM CGP EA cartesian genetic programming

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Enhancing cartesian genetic programming through preferential selection of larger solutions

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EVOLUTIONARY INTELLIGENCE 2021年第4期14卷 1539-1546页

作者： Milano, Nicola Nolfi, Stefano Natl Res Council CNR Inst Cognit Sci & Technol Via S Martino della Battaglia 44 I-00185 Rome Italy

We demonstrate how the efficiency of cartesian genetic programming methods can be enhanced through the preferential selection of phenotypically larger solutions among equally good solutions. The advantage is demonstrated in two qualitatively different problems: the eight-bit parity problems and the "Paige" regression problem. In both cases, the preferential selection of larger solutions provides an advantage in term of the performance and of speed, i.e. number of evaluations required to evolve optimal or high-quality solutions. Performance can be further enhanced by self-adapting the mutation rate through the one-fifth success rule. Finally, we demonstrate that, for problems like the Paige regression in which neutrality plays a smaller role, performance can be further improved by preferentially selecting larger solutions also among candidates with similar fitness.

关键词： Evolvability Robustness cartesian genetic programming Boolean circuits Regression problem Even-parity problem

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MT-CGP: Mixed Type cartesian genetic programming 12

MT-CGP: Mixed Type Cartesian Genetic Programming

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14th International Conference on genetic and Evolutionary Computation Conference (GECCO)

作者： Harding, Simon Graziano, Vincent Leitner, Juergen Schmidhuber, Juergen IDSIA SUPSI USI CH-6928 Manno Switzerland

ISBN: (纸本)9781450311779

The majority of genetic programming implementations build expressions that only use a single data type. This is in contrast to human engineered programs that typically make use of multiple data types, as this provides the ability to express solutions in a more natural fashion. In this paper, we present a version of cartesian genetic programming that handles multiple data types. We demonstrate that this allows evolution to quickly find competitive, compact, and human readable solutions on multiple classification tasks.

关键词： cartesian genetic programming Classifiers

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