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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Harrison, Joe Alderliesten, Tanja Bosman, Peter A. N. Ctr Wiskunde & Informat Amsterdam Netherlands Delft Univ Technol Delft Netherlands Leiden Univ Med Ctr Leiden Netherlands

ISBN: (纸本)9783031147210;9783031147203

genetic programming (GP) can make an important contribution to explainable artificial intelligence because it can create symbolic expressions as machine learning models. Nevertheless, to be explainable, the expressions must not become too large. This may, however, limit their potential to be accurate. The re-use of subexpressions has the unique potential to mitigate this issue. The genetic programming Gene-pool Optimal Mixing Evolutionary Algorithm (GP-GOMEA) is a recent model-based GP approach that has been found particularly capable of evolving small expressions. However, its tree representation offers no explicit mechanisms to re-use subexpressions. By contrast, the graph representation in cartesian GP (CGP) is natively capable of re-use. For this reason, we introduce CGP-GOMEA, a variant of GP-GOMEA that uses graphs instead of trees. We experimentally compare various configurations of CGP-GOMEA with GP-GOMEA and find that CGP-GOMEA performs on par with GP-GOMEA on three common datasets. Moreover, CGP-GOMEA is found to produce models that re-use subexpressions more often than GP-GOMEA uses duplicate subexpressions. This indicates that CGP-GOMEA has unique added potential, allowing to find even smaller expressions than GP-GOMEA with similar accuracy.

关键词： cartesian genetic programming Gene-pool Optimal Mixing Subexpression re-use Evolutionary computation Symbolic regression

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Investigating the Baldwin Effect on cartesian genetic programming Efficiency

Investigating the Baldwin Effect on Cartesian Genetic Progra...

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IEEE Congress on Evolutionary Computation

作者： Khatir, Mehrdad Jahangir, Amir Hossein Beigy, Hamid Sharif Univ Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781424418220

cartesian genetic programming (CGP) has an unusual genotype representation which makes it more efficient than genetic programming (GP) in digital circuit design problem. However, to the best of our knowledge, all methods used in evolutionary design of digital circuits deal with rugged, complex search space, which results in long running time to obtain successful evolution. Therefore, employing a method to guide evolution in these spaces can facilitate achieving more reasonable results. It has been claimed that a two-step evolutionary scenario caused by benefit and cost of learning called Baldwin effect can guide evolution in the biology and artificial life. Therefore, we have been motivated to examine this effect on CGP. We observe using this scenario the success rate and evolution time of CGP improves dramatically especially when size of chromosomes increases.

关键词： cartesian genetic programming Baldwin Effect Phenotypic Plasticity Digital Circuit Reinforcement Learning

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Towards Discrete Phenotypic Recombination in cartesian genetic programming 17th

Towards Discrete Phenotypic Recombination in Cartesian Genet...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Kalkreuth, Roman TU Dortmund Univ Dept Comp Sci Chair 11 Algorithm Engn Computat Intelligence Res Grp Dortmund North Rhine Wes Germany

ISBN: (纸本)9783031147210;9783031147203

The tree-based representation model of genetic programming (GP) is largely used with subtree crossover for genetic variation. Unlike cartesian genetic programming (CGP) which is commonly used merely with mutation. Compared to comprehensive knowledge about recombination in the field of tree-based GP, the state of knowledge in CGP appears to be comparatively poor. Even if CGP was officially introduced over twenty years ago, the role of recombination in CGP has been recently considered an open issue. Several promising steps have been taken in recent years, but more research is needed to develop towards a more comprehensive and holistic perspective on crossover in CGP. In this work, we propose a phenotypic variation method for discrete recombination in CGP. We compare our method to the traditional mutation-only CGP approach on a set of well-known symbolic regression problems. The initial results presented in this work demonstrate that the use of our proposed discrete recombination method performs significantly better than the traditional mutation-only approach.

关键词： cartesian genetic programming Crossover Phenotypic variation

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Improving Image Filters with cartesian genetic programming 13th

Improving Image Filters with Cartesian Genetic Programming

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13th International Joint Conference on Computational Intelligence (IJCCI) / 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Biau, Julien Wilson, Dennis Cussat-Blanc, Sylvain Luga, Herve Kawantech Toulouse France ISAE SUPAERO Toulouse France Univ Toulouse Toulouse France

ISBN: (纸本)9789897585340

The automatic construction of an image filter is a difficult task for which many recent machine learning methods have been proposed. However, these approaches, such as deep learning, do not allow for the filter to be understood, and they often replace existing filters designed by human engineers without building on this expertise. genetic improvement offers an alternative approach to construct understandable image filter programs and to build them by improving existing systems. In this paper, we propose a method for genetic improvement of image filters using cartesian genetic programming We introduce two operators for genetic improvement which allow insertion and deletion of a node in the graph in order to quickly improve a given filter. These new operators are tested in three different datasets starting from published or engineered filters. We show that insertion and deletion operators improve the performance of CGP to produce newly adapted filters.

关键词： genetic programming cartesian genetic programming Image Processing genetic Improvement

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Multitask Evolution with cartesian genetic programming 17

Multitask Evolution with Cartesian Genetic Programming

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genetic and Evolutionary Computation Conference (GECCO)

作者： Scott, Eric O. De Jong, Kenneth A. George Mason Univ 4400 Univ Dr Fairfax VA 22030 USA

We introduce a genetic programming method for solving multiple Boolean circuit synthesis tasks simultaneously. This allows us to solve a set of elementary logic functions twice as easily as with a direct, single-task ... 详细信息

ISBN: (纸本)9781450349390

关键词： cartesian genetic programming Multitask Learning

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Continuous cartesian genetic programming based representation for multi-objective neural architecture search

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APPLIED SOFT COMPUTING 2023年 147卷

作者： Garcia-Garcia, Cosijopii Morales-Reyes, Alicia Escalante, Hugo Jair INAOE Comp Sci Luis Enrique Erro 1 Cholula 72840 Puebla Mexico

We propose a novel neural architecture search (NAS) approach for the challenge of designing convolutional neural networks (CNNs) that achieve a good tradeoff between complexity and accuracy. We rely on cartesian genetic programming (CGP) and integrated real-based and block-chained CNN representation, for optimization using multi-objective evolutionary algorithms (MOEAs) in the continuous domain. We introduce two variants, CGP-NASV1 and CGP-NASV2, which differ in the granularity of their respective search spaces. To evaluate the proposed algorithms, we utilized the non-dominated sorting genetic algorithm II (NSGA-II) on the CIFAR-10, CIFAR-100,and SVHN datasets. Additionally, we extended the empirical analysis while maintaining the same solution representation to assess other searching techniques such as differential evolution (DE), the multi-objective evolutionary algorithm based on decomposition (MOEA/D), and the S metric selection evolutionary multi-objective algorithm (SMS-EMOA). The experimental results demonstrate that our approach exhibits competitive classification performance and model complexity compared to state-of-the-art methods.(c) 2023 Elsevier B.V. All rights reserved.

关键词： Neural architecture search cartesian genetic programming Convolutional neural network Multi-objective optimization

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CGenProg: Adaptation of cartesian genetic programming with migration and opposite guesses for automatic repair of software regression faults

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 169卷 114503-114503页

作者： Khalilian, Alireza Baraani-Dastjerdi, Ahmad Zamani, Bahman Univ Isfahan Fac Comp Engn Dept Software Engn Esfahan Iran

In the last decade, the research community has been actively working to develop the techniques that can automatically find a solution to a software fault, namely, automatic program repair (APR). As of today, a multitude of APR techniques has been proposed. The techniques could have effectively repaired a wide variety of fault classes. However, the development of effective APR techniques for software regression faults, which are prevalently occurred in the maintenance stage of the software lifecycle, have received little attention. By incorporating specific knowledge in the domain of software regression faults, we have developed a novel technique for automatic repair of software regression faults in Java programs, which we call CGenProg. To achieve this, we have extensively adapted and modified the original cartesian genetic programming (CGP), biogeography-based optimization (BBO), and opposition-based learning (OBL). The modified CGP serves us as the core evolutionary process while the modified BBO and OBL act as crossover and mutation, respectively. The significance of CGenProg is that it contributes to the solution of a practical problem faced by developers in the maintenance stage of the software lifecycle. For expert and intelligent systems, it extends what is known about the application of optimization algorithms in the context of APR. Further, it demonstrates a novel use of CGP, BBO, and OBL for automatic repair of software regression faults. To evaluate CGenProg, we have developed a prototype tool using the Java language. Then, we conducted experiments on several programs in Code4Bench where each program is released with multiple consecutive versions comprising software regression faults. In the experiments, CGenProg could repair 17 out of 30 faulty programs. We conclude that CGenProg proves relevant and effective for repairing software regression faults. The impact of this study is to incentivize researchers for further exploitation and adaptation of the wea

关键词： Software regression testing cartesian genetic programming Automatic program repair Opposition-based learning Biogeography-based optimization

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Using Multivalued cartesian genetic programming (M-CGP) for Automatic Design of Digital Sequential Circuits

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APPLIED SCIENCES-BASEL 2024年第23期14卷 11153页

作者： Jamroz, Dariusz AGH Univ Krakow Fac Elect Engn Automat Comp Sci & Biomed Engn Dept Appl Comp Sci Al Mickiewicza 30 PL-30059 Krakow Poland

The paper addresses the problem of the automatic design of sequential systems. For a complete description of the operation of the sequential system, a table of states or another representation of transition graphs describing possible changes in system states is necessary. This paper adopts a completely different approach, in which the description of the sequential system results from the study of the responses to signals given from outside and from an unknown system, which is treated as a black box. This approach may be useful when we want to recreate the internal structure of a given, unknown system or when we want to obtain a system based only on the information about the system's reactions to given external signals, without going into the principles of its operation. The paper presents problems that arise when creating the data strings that describe the reactions of the designed system and ways for solving these problems, and it presents Multivalued cartesian genetic programming (M-CGP)-a new approach used to design sequential circuits. Further research has developed a system based on this model. The paper presents examples of obtained sequential systems generated using the newly created system.

关键词： genetic programming cartesian genetic programming CGP sequential circuits sequential logic circuit M-CGP digital circuits design

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Recent Developments in cartesian genetic programming and its Variants

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ACM COMPUTING SURVEYS 2019年第6期51卷 122-122页

作者： Manazir, Abdul Raza, Khalid Jamia Millia Islamia Dept Comp Sci New Delhi 110025 India

cartesian genetic programming (CGP) is a variant of genetic programming with several advantages. During the last one and a half decades, CGP has been further extended to several other forms with lots of promising advantages and applications. This article formally discusses the classical form of CGP and its six different variants proposed so far, which include Embedded CGP, Self-Modifying CGP, Recurrent CGP, Mixed-Type CGP, Balanced CGP, and Differential CGP. Also, this article makes a comparison among these variants in terms of population representations, various constraints in representation, operators and functions applied, and algorithms used. Further, future work directions and open problems in the area have been discussed.

关键词： cartesian genetic programming evolutionary computing genetic programming machine learning bloat

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Control System Synthesis by Means of cartesian genetic programming

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Procedia Computer Science 2017年 103卷 176-182页

作者： G.I. Balandina Peoples’ Friendship University of Russia 6 Miklukho-Maklaya str. Moscow 117198 Russia

cartesian genetic programming (CGP) is a type of genetic programming based on a program in a form of a directed graph. It also belongs to the methods of Symbolic Regression allowing to receive the optimal mathematical expression for a problem. Nowadays it becomes possible to use computers very effectively for symbolic regression calculations. CGP was developed by Julian Miller in 1999-2000. It represents a program for decoding a genotype (string of integers) into the phenotype (graph). The nodes of that graph contain references to functions from a function table, which could contain arithmetic, logical operations and/or user-defined functions. The inputs of those functions are connected to the node inputs, which itself could be connected to a node output or a graph input. As a result, it's possible to construct several mathematical expressions for the outputs and calculate them for the given inputs. This CGP implementation use point mutation to form new mathematical expressions. Steady-state genetic algorithm is chosen as a search engine. Solution solving the control system synthesis problem is presented in a form of the Pareto set, which contains a set of satisfactory control functions. Nonlinear Duffing oscillator is taken as a dynamic object.

关键词： Optimal control synthesis nonlinear control systems cartesian genetic programming genetic programming

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