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Using FPGA devices to accelerate the evaluation phase of tree-based genetic programming: an extended analysis

genetic programming AND EVOLVABLE MACHINES 2025年第1期26卷 1-48页

作者： Crary, Christopher Piard, Wesley Stitt, Greg Hicks, Benjamin Bean, Caleb Burlacu, Bogdan Banzhaf, Wolfgang Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA Univ Appl Sci Upper Austria Heurist & Evolutionary Algorithms Lab Hagenberg Upper Austria Austria Michigan State Univ Dept Comp Sci & Engn E Lansing MI USA

This paper establishes the potential of accelerating the evaluation phase of tree-based genetic programming through contemporary field-programmable gate array (FPGA) technology. This exploration stems from the fact that FPGAs can sometimes leverage increased levels of both data and function parallelism, as well as superior power/energy efficiency, when compared to general-purpose CPU/GPU systems. In this investigation, we introduce a fixed-depth, tree-based architecture that can fully parallelize tree evaluation for type-consistent primitives that are unrolled and pipelined. We show that our accelerator on a 14nm FPGA achieves an average speedup of 43x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} when compared to a recent open-source GPU solution, TensorGP, implemented on 8nm process-node technology, and an average speedup of 4,902x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} when compared to a popular baseline GP software tool, DEAP, running parallelized across all cores of a 2-socket, 28-core (56-thread), 14nm CPU server. Despite our single-FPGA accelerator being 2.4x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document} slower on average when compared to the recent state-of-the-art Operon tool executing on the same 2-processor, 28-core CPU system, we show that this single-FPGA system is 1.4x\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym}

关键词： tree-based genetic programming Field-programmable gate array Domain-specific architecture Hardware acceleration

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Efficient tree traversal to reduce code growth in tree-based genetic programming

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JOURNAL OF HEURISTICS 2009年第1期15卷 77-104页

作者： Wyns, Bart Boullart, Luc Univ Ghent Dept Elect Energy Syst & Automat B-9052 Zwijnaarde Belgium

genetic programming is an evolutionary optimization method following the principle of program induction. genetic programming often uses variable-length tree structures for representing candidate solutions. A serious problem with variable-length representations is code growth: during evolution these tree structures tend to grow in size without a corresponding increase in fitness. Many anti-bloat methods focus solely on size reduction and forget about fitness improvement, which is rather strange when using an "optimization" method. This paper reports the application of a semantically driven local search operator to control code growth and improve best fitness. Five examples, two theoretical benchmark applications and three real-life test problems are used to illustrate the obtained size reduction and fitness improvement. Performance of the local search operator is also compared with various other anti-bloat methods such as size and depth delimiters, an expression simplifier, linear and adaptive parsimony pressure, automatically defined functions and Tarpeian bloat control.

关键词： tree traversal Subtree fitness Code growth Local optimization tree-based genetic programming

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Compositional kernel learning using tree-based genetic programming for Gaussian process regression

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2020年第3期62卷 1313-1351页

作者： Jin, Seung-Seop KICT Sustainable Infrastruct Res Ctr Goyang Si 10223 South Korea

Although Gaussian process regression (GPR) is a powerful Bayesian nonparametric regression model for engineering problems, its predictive performance is highly dependent on a kernel for covariance function of GPR. However, choosing a proper kernel is still challenging even for experts. To choose proper kernel automatically, this study proposes a compositional kernel (CPK) learning using tree-based genetic programming (GEP). The optimal structure of the kernel is defined as a compositional representation based on sums and products of eight base-kernels. The CPK can be encoded as a tree-structure, so that tree-based GEP is employed to discover an optimal tree-structure of the CPK. To avoid overly complex solution in GEP, the proposed method introduced a dynamic maximum tree-depth technique. The novelty of the proposed method is to utilize more flexible and efficient learning capability to learn the relationship between input and output than existing methods. To evaluate the learning capability of the proposed method, seven test functions were firstly investigated with various noise levels, and its predictive accuracy was compared with existing methods. Reliability problems in both parallel and series systems were introduced to evaluate the performance of the proposed method for efficient reliability assessment. The results show that the proposed method generally outperforms or performs similarly to the best one among existing methods. In addition, it is also shown that proper kernel function can significantly improve the performance of GPR as the training data increases. Stated differently, the proposed method can learn the function of being fitted efficiently with less training samples than existing methods. In this context, the proposed method can make powerful and automatic predictive modeling based on GPR in engineering problems.

关键词： Gaussian processes regression Compositional kernel learning tree-based genetic programming Surrogate modeling Reliability analysis

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Depth-control strategies for crossover in tree-based genetic programming

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SOFT COMPUTING 2011年第9期15卷 1865-1878页

作者： Xie, Huayang Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand Anhui Polytech Univ Dept Comp & Informat Sci Maanshan Anhui Peoples R China

The standard subtree crossover operator in the tree-based genetic programming (GP) has been considered as problematic. In order to improve the standard subtree crossover, controlling depth of crossover points becomes a research topic. However, the existence of many different and inconsistent crossover depth-control schemes and the possibility of many other depth-control schemes make the identification of good depth-control schemes a challenging problem. This paper aims to investigate general heuristics for making good depth-control schemes for crossover in tree-based GP. It analyses the patterns of depth of crossover points in good predecessor programs of five GP systems that use the standard subtree crossover and four approximations of the optimal crossover operator on three problems in different domains. The analysis results show that an effective depth-control scheme is problem-dependent and evolutionary stage-dependent, and that good crossover events have a strong preference for roots and (less strongly) bottoms of parent program trees. The results also show that some ranges of depths between the roots and the bottoms are also preferred, suggesting that unequal-depth-selection-probability strategies are better than equal-depth-selection-probability strategies.

关键词： Crossover tree-based genetic programming

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Using FPGA Devices to Accelerate tree-based genetic programming: A Preliminary Exploration with Recent Technologies 6th

Using FPGA Devices to Accelerate Tree-Based Genetic Programm...

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26th European Conference on genetic programming (EuroGP) Held as Part of EvoStar

作者： Crary, Christopher Piard, Wesley Stitt, Greg Bean, Caleb Hicks, Benjamin Univ Florida Gainesville FL 32611 USA

ISBN: (纸本)9783031295720;9783031295737

In this paper, we explore the prospect of accelerating tree-based genetic programming (TGP) by way of modern field-programmable gate array (FPGA) devices, which is motivated by the fact that FPGAs can sometimes leverage larger amounts of data/function parallelism, as well as better energy efficiency, when compared to general-purpose CPU/GPU systems. In our preliminary study, we introduce a fixed-depth, tree-based architecture capable of evaluating type-consistent primitives that can be fully unrolled and pipelined. The current primitive constraints preclude arbitrary control structures, but they allow for entire programs to be evaluated every clock cycle. Using a variety of floating-point primitives and random programs, we compare to the recent TensorGP tool executing on a modern 8nm GPU, and we show that our accelerator implemented on a 14 nm FPGA achieves an average speedup of 43x. When compared to the popular baseline tool DEAP executing across all cores of a 2-socket, 28-core (56-thread), 14nm CPU server, our accelerator achieves an average speedup of 4,902x. Finally, when compared to the recent state-of-the-art tool Operon executing on the same 2-processor CPU system, our accelerator executes about 2.4x slower on average. Despite not achieving an average speedup over every tool tested, our single-FPGA accelerator is the fastest in several instances, and we describe five future extensions that could allow for a 32-144x speedup over our current design as well as allow for larger program depths/sizes. Overall, we estimate that a future version of our accelerator will constitute a state-of-the-art GP system for many applications.

关键词： tree-based genetic programming Field-programmable gate arrays Hardware acceleration

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EASEA Parallelization of tree-based genetic programming

EASEA Parallelization of Tree-Based Genetic Programming

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2010 IEEE World Congress on Computational Intelligence

作者： Maitre, Ogier Querry, Stephane Lachiche, Nicolas Collet, Pierre Univ Strasbourg LSIIT Image Sci Comp Sci & Remote Sensing Lab F-67400 Illkirch Graffenstaden France

ISBN: (纸本)9781424481262

This paper introduces the implementation of Koza-style tree-based genetic programming on General Purpose Graphic Processing Units (GPGPU) using the EASEA language, and shows how a GP algorithm can be easily implemented using EASEA and CUDA. Performance is first discussed on a classical toy problem taken from one of Koza's books and then on a real world problem inspired from aeronautics, that extends the results to difficult problems with large data sets.

关键词： CUDA EASEA parallelization GPGPU Koza-style tree coprocessors general purpose graphic processing unit genetic algorithms tree-based genetic programming trees (mathematics)

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Proposal and Preliminary Investigation of a Fitness Function for Partial Differential Models 18

Proposal and Preliminary Investigation of a Fitness Function...

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18th European Conference on genetic programming (EuroGP)

作者： Peretta, Igor S. Yamanaka, Keiji Bourgine, Paul Collet, Pierre Univ Fed Uberlandia Fac Elect Engn BR-38400 Uberlandia MG Brazil CS DC UNESCO UniTwin ECCE E Lab Strasbourg France Strasbourg Univ ICUBE Lab Strasbourg France Open Univ Fac Math Comp & Technol Milton Keynes MK7 6AA Bucks England

ISBN: (纸本)9783319165011;9783319165004

This work proposes and presents a preliminary investigation of a fitness evaluation scheme supported by a proper genotype representation intended to guide an under development expansion to EASEA/EASEA-CLOUD platforms to evolve partial differential equations as models for a specific system of interest, starting with measures from that system. A simple proof of concept using a dynamic bidirectional surface wave is presented, showing that the proposed fitness evaluation scheme is very promising to enable automate system modelling, even when dealing with up to +/- 10% noise-added data.

关键词： System modelling Partial differential equations Fitness function Galerkin's method Jacobi-Legendre polynomials tree-based genetic programming

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