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Genetic and Evolutionary Computation Conference (GECCO)

作者： Schweim, Dirk Rothlauf, Franz Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9781450356183

estimation of distribution programming (EDP) replaces standard GP variation operators with sampling from a learned probability model. To ensure a minimum amount of variation in a population, EDP adds random noise to the probabilities of random variables. This paper studies the bias of EDP's variation operator by performing random walks. The results indicate that the complexity of the EDP model is high since the model is overfitting the parent solutions when no additional noise is being used. Adding only a low amount of noise leads to a strong bias towards small trees. The bias gets stronger with an increased amount of noise. Our findings do not support the hypothesis that sampling drift is the reason for the loss of diversity. Furthermore, we suggest using property vectors to study the bias of variation operators. Property vectors can represent the distribution of a population's relevant property, such as tree depth or tree size. The Bhattacharyya coefficient of two property vectors is a measure of the similarity of the two distributions of population properties. The results for EDP and standard GP illustrate that search bias can be assessed by representing distributions using property vectors and measuring their similarity using the Bhattacharyya coefficient.

关键词： Search Bias Genetic programming estimation of distribution programming estimation of distribution Algorithm n-Gram Similarity

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A hierarchical estimation of multi-modal distribution programming for regression problems

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KNOWLEDGE-BASED SYSTEMS 2023年 260卷

作者： Koosha, Mohaddeseh Khodabandelou, Ghazaleh Ebadzadeh, Mohammad Mehdi Amirkabir Univ Technol Tehran Iran Univ Paris Est Creteil UPEC LISSI Lab Creteil France

estimation of distribution programming is an iterative method to evolve program trees. It estimates the distribution of the most suitable program trees and then produces a new generation of program trees by sampling from the distribution. This paper proposes a hierarchical estimation of multimodal distribution programming (HEMMDP). First, the population is divided into K subpopulations by a clustering algorithm where the distribution of each subpopulation is modified according to an objective function. Then, at each generation, a new subpopulation is generated from the modified distribution. The objective function aims to gradually improve the fitness of the program trees in each subpopulation. Finally, the appropriate program trees are added as new terminal nodes to the terminal set, resulting in a new hierarchy. The best-fitting program trees from each subpopulation with high synergistic value are chosen as basis functions. The proposed approach uses a linear function of the basis functions to solve the regression problem. The proposed method is evaluated on several real-world benchmark datasets. The datasets are divided into four classes: small-difficult, small-easy, large-difficult, and large-easy. The proposed method improves the results of the best methods for the regression problem by 232% and 62% for small difficult data sets and large difficult data sets, respectively.(c) 2022 Elsevier B.V. All rights reserved.

关键词： estimation of distribution programming Genetic programming Program Trees Regression

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Probabilistic Contextual and Structural Dependencies Learning in Grammar-Based Genetic programming

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EVOLUTIONARY COMPUTATION 2021年第2期29卷 239-268页

作者： Wong, Pak-Kan Wong, Man-Leung Leung, Kwong-Sak Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Lingnan Univ Dept Comp & Decis Sci Hong Kong Peoples R China

Genetic programming is a method to automatically create computer programs based on the principles of evolution. The problem of deceptiveness caused by complex dependencies among components of programs is challenging. It is important because it can misguide Genetic programming to create suboptimal programs. Besides, aminor modification in the programs may lead to a notable change in the program behaviours and affect the final outputs. This article presents Grammar-Based Genetic programming with Bayesian Classifiers (GBGPBC) in which the probabilistic dependencies among components of programs are captured using a set of Bayesian network classifiers. Our system was evaluated using a set of benchmark problems (the deceptive maximum problems, the royal tree problems, and the bipolar asymmetric royal tree problems). It was shown to be often more robust and more efficient in searching the best programs than other related Genetic programming approaches in terms of the total number of fitness evaluation. We studied what factors affect the performance of GBGPBC and discovered that robust variants of GBGPBC were consistently weakly correlated with some complexity measures. Furthermore, our approach has been applied to learn a ranking program on a set of customers in direct marketing. Our suggested solutions help companies to earn significantly more when compared with other solutions produced by several well-known machine learning algorithms, such as neural networks, logistic regression, and Bayesian networks.

关键词： estimation of distribution programming adaptive grammar-based genetic programming Bayesian network classifier data mining

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Learning Grammar Rules in Probabilistic Grammar-Based Genetic programming 5th

Learning Grammar Rules in Probabilistic Grammar-Based Geneti...

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5th International Conference on the Theory and Practice of Natural Computing (TPNC)

作者： Wong, Pak-Kan Wong, Man-Leung Leung, Kwong-Sak Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China Lingnan Univ Tuen Mun Hong Kong Peoples R China

ISBN: (纸本)9783319490014;9783319490007

Grammar-based Genetic programming (GBGP) searches for a computer program in order to solve a given problem. Grammar constrains the set of possible programs in the search space. It is not obvious to write an appropriate grammar for a complex problem. Our proposed Bayesian Grammar-Based Genetic programming with Hierarchical Learning (BGBGP-HL) aims at automatically designing new rules from existing relatively simple grammar rules during evolution to improve the grammar structure. The new grammar rules also reflects the new understanding of the existing grammar under the given fitness evaluation function. Based on our case study in asymmetric royal tree problem, our evaluation shows that BGBGP-HL achieves the best performance among the competitors. Compared to other algorithms, search performance of BGBGP-HL is demonstrated to be more robust against dependencies and the changes in complexity of programs.

关键词： Genetic programming estimation of distribution programming Adaptive grammar Bayesian network

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Probabilistic Grammar-based Deep Neuroevolution 19

Probabilistic Grammar-based Deep Neuroevolution

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Wong, Pak-Kan Wong, Man-Leung Leung, Kwong-Sak Chinese Univ Hong Kong Hong Kong Peoples R China Lingnan Univ Hong Kong Peoples R China

ISBN: (纸本)9781450367486

Designing deep neural networks by human engineers can be challenging because there are various choices of deep neural network structures. We developed a deep neuroevolution system to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using a probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic programming. Our approach takes advantage of the probabilistic dependencies found among the structures of networks. The system was applied to tackle the problem of the physiological signal classification of abnormal heart rhythm. In the classification problem, our discovered model is more accurate than AlexNet. Our discovered model uses about 2% of the total amount of parameters of AlexNet.

关键词： estimation of distribution programming Deep Neural Network

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Hierarchical Knowledge in Self-Improving Grammar-Based Genetic programming

Hierarchical Knowledge in Self-Improving Grammar-Based Genet...

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14th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Wong, Pak-Kan Wong, Man-Leung Leung, Kwong-Sak Chinese Univ Hong Kong Sha Tin Hong Kong Peoples R China Lingnan Univ Tuen Mun Hong Kong Peoples R China

ISBN: (纸本)9783319458236;9783319458229

Structure of a grammar can influence how well a Grammar-Based Genetic programming system solves a given problem but it is not obvious to design the structure of a grammar, especially when the problem is large. In this paper, our proposed Bayesian Grammar-Based Genetic programming with Hierarchical Learning (BGBGP-HL) examines the grammar and builds new rules on the existing grammar structure during evolution. Once our system successfully finds the good solution(s), the adapted grammar will provide a grammar-based probabilistic model to the generation process of optimal solution(s). Moreover, our system can automatically discover new hierarchical knowledge (i.e. how the rules are structurally combined) which composes of multiple production rules in the original grammar. In the case study using deceptive royal tree problem, our evaluation shows that BGBGP-HL achieves the best performance among the competitors while it is capable of composing hierarchical knowledge. Compared to other algorithms, search performance of BGBGP-HL is shown to be more robust against deceptiveness and complexity of the problem.

关键词： Genetic programming Hierarchical knowledge learning estimation of distribution programming Adaptive grammar Bayesian network

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