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Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic

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Procedia CIRP 2019年 79卷 439-444页

作者： Yong Zhou Jian-jun Yang School of Mechanical Engineering and Automation Beihang University Beijing 100191 China

This study proposes four multi-objective genetic programming based hyper-heuristic methods(MO-GPHH) for automated heuristic design to solve the multi-objective dynamic flexible job shop scheduling problem(MO-DFJSP). A scheduling policy(SP) used in the MO-DFJSP includes two decision rules: a job sequencing rule(JSR) and a machine assignment rule(MAR). These two rules are simultaneously evolved to solve three scheduling objectives (mean weighted tardiness, maximum tardiness and mean flow time). The results demonstrate that the pareto front of the proposed methods dominate that of 320 human-made SPs which are selected from literatures on training set, and the evolved SPs outperform manual SPs in 58/64 test scenarios.

关键词： dynamic flexible job shop scheduling scheduling policies multi-objective genetic programming cooperative coevolution

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AUTONOMOUS ROBOT FAILURE RECOGNITION DESIGN USING multi-objective genetic programming

AUTONOMOUS ROBOT FAILURE RECOGNITION DESIGN USING MULTI-OBJE...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YANG ZHANG Electronic and Electrical Engineering Department The University of Sheffield S1 3JD UK

An evolutionary autonomous failure recognition approach is presented using multi-objective genetic programming in this paper. It is compared with the conventional robot failure classification algorithm. Detailed analysis of the evolved feature extractors is tempted on investigated problems. We conclude MOGP is an effective and practical way to automate the process of failure recognition system design with better recognition accuracy and more flexibility via optimizing feature extraction stage.

关键词： Feature Extraction Autonomous robot Failure recognition multi-objective genetic programming

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Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection 05

Evolving optimal feature extraction using multi-objective ge...

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Proceedings of the 7th annual conference on genetic and evolutionary computation

作者： Yang Zhang Peter I. Rockett University of Sheffield UK

ISBN: (纸本)9781595930101

In this paper we describe a generic methodology to create an "optimal" feature extraction pre-processing stage for pattern classification. Our aim is to map the input data into a new, one-dimensional feature space in which separability is maximized under a simple thresholding classification. We have used multi-objective genetic programming with Pareto strength-based ranking to bias the selection procedure. The methodology is applied to the edge detection problem in image processing; we make quantitative comparison with the pre-processing stages of the well-known Canny edge detector using synthetic and real-world edge data and conclude that the performance of our evolutionary-based method is much superior to the Canny algorithm based on the criterion of minimum Bayes risk.

关键词： edge detector multi-objective genetic programming feature extractor

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A Semantic genetic programming Approach to Evolving Heuristics for multi-objective Dynamic Scheduling 36th

A Semantic Genetic Programming Approach to Evolving Heuristi...

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36th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Xu, Meng Mei, Yi Zhang, Fangfang Zhang, Mengjie Victoria Univ Wellington Ctr Data Sci & Artificial Intelligence Wellington 6140 New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington 6140 New Zealand

ISBN: (纸本)9789819983902;9789819983919

multi-objective dynamic flexible job shop scheduling (MO-DFJSS) is a challenging problem that requires finding high-quality schedules for jobs in a dynamic and flexible manufacturing environment, considering multiple potentially conflicting objectives simultaneously. A good approach to MO-DFJSS is to combine genetic programming (GP) with Non-dominated Sorting genetic Algorithm II (NSGA-II), namely NSGP-II, to evolve a set of non-dominated scheduling heuristics. However, a limitation of NSGPII is that individuals with different genotypes can exhibit the same behaviour, resulting in a loss of population diversity. Semantic genetic programming (SGP) considers individual semantics during the evolutionary process and can enhance population diversity in various domains. However, its application in the domain of MO-DFJSS remains unexplored. Therefore, it is worthy to incorporate semantic information with NSGPII for MO-DFJSS. This study focuses on semantic diversity and semantic similarity. The results demonstrate that NSGPII considering semantic diversity yields better performance compared with the original NSGPII. Moreover, NSGPII incorporating semantic similarity achieves even better performance, highlighting the importance of maintaining a reasonable semantic distance between offspring and their parents. Further analysis reveals that the improved performance achieved by the proposed methods is attributed to the attainment of a more semantically diverse population through effective control of semantic distances between individuals.

关键词： Heuristic learning multi-objective genetic programming Semantic multi-objective dynamic scheduling

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A New multiobjective genetic programming for Extraction of Design Information from Non-dominated Solutions

A New Multiobjective Genetic Programming for Extraction of D...

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7th International Conference on Eevolutionary Mmulti-Criterion Optimization (EMO)

作者： Tatsukawa, Tomoaki Nonomura, Taku Oyama, Akira Fujii, Kozo Japan Aerosp Explorat Agcy Inst Space & Astronaut Sci Sagamihara Kanagawa 2525210 Japan

ISBN: (纸本)9783642371400

We propose a new type of multi-objective genetic programming (MOGP) for multi-objective design exploration (MODE). The characteristic of the new MOGP is the simultaneous symbolic regression to multiple objective functions using correlation coefficients. This methodology is applied to non-dominated solutions of the multi-objective design optimization problem to extract information between objective functions and design parameters. The result of MOGP is symbolic equations that are highly correlated to each objective function through a single GP run. These equations are also highly correlated to several objective functions. The results indicate that the proposed MOGP is capable of finding new design parameters more closely related to the objective functions than the original design parameters. The proposed MOGP is applied to the test problem and the practical design problem to evaluate the capability.

关键词： multi-objective genetic programming multi-objective Design Explolation CFD NSGA-II

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A genetic programming Approach to Generating Musical Compositions 1

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4th International Conference and 13th European Event on Evolutionary and Biologically Inspired Music, Sound, Art, and Design (EvoMUSART)

作者： Hofmann, David M. Univ Mus Karlsruhe Inst Musicol & Mus Informat Karlsruhe Germany

ISBN: (数字)9783319164984

ISBN: (纸本)9783319164984;9783319164977

Evolutionary algorithms have frequently been applied in the field of computer-generated art. In this paper, a novel approach in the domain of automated music composition is proposed. It is inspired by genetic programming and uses a tree-based domain model of compositions. The model represents musical pieces as a set of constraints changing over time, forming musical contexts allowing to compose, reuse and reshape musical fragments. The system implements a multi-objective optimization aiming for statistical measures and structural features of evolved models. Furthermore a correspondent domain-specific computer language is introduced used to transform domain models to a comprehensive, human-readable text representation and vice versa. The language is also suitable to limit the search space of the evolution and as a composition language for human composers.

关键词： Automated music generation multi-objective genetic programming Domain-specific languages

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Spectral acceleration prediction using genetic programming based approaches

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APPLIED SOFT COMPUTING 2021年 106卷 107326-107326页

作者： Gandomi, Mostafa Kashani, Ali R. Farhadi, Ali Akhani, Mohsen Gandomi, Amir H. Univ Tehran Dept Civil Engn Tehran Iran Univ Memphis Dept Civil Engn Memphis TN USA Univ Technol Sydney Fac Engn & IT Ultimo Australia

Evolutionary computation (EC) is a widely used computational intelligence that facilitates the formulation of a range of complex engineering problems. This study tackled two hybrid EC techniques based on genetic programming (GP) for ground motion prediction equations (GMPEs). The first method coupled regression analysis with multi-objective genetic programming. In this way, the strategy was maximizing the accuracy and minimizing the models' complexity simultaneously. The second approach incorporated mesh adaptive direct search (MADS) into gene expression programming to optimize the obtained coefficients. A big data set provided by the Pacific Earthquake Engineering Research Centre (PEER) was used for the model development. Two explicit formulations were developed during this effort. In those formulae, we correlated spectral acceleration to a set of seismological parameters, including the period of vibration, magnitude, the closest distance to the fault ruptured area, shear wave velocity averaged over the top 30 meters, and style of faulting. The GP-based models are verified by a comprehensive comparison with the most well-known methods for GMPEs. The results show that the proposed models are quite simple and straightforward. The high degrees of accuracy of the predictions are competitive with the NGA complex models. Correlations of the predicted data using GEP-MADs and MOGP-R models with the real observations seem to be better than those available in the literature. Three statistical measures for GMPEs, such as E (%), LLH, and EDR index, confirmed those observations. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Spectral acceleration Ground-motion models multi-gene genetic programming Gene expression programming multi-objective genetic programming

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Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems

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APPLIED SOFT COMPUTING 2018年 69卷 610-624页

作者： Kronberger, Gabriel Kommenda, Michael Lughofer, Edwin Saminger-Platz, Susanne Promberger, Andreas Nickel, Falk Winkler, Stephan Affenzeller, Michael Univ Appl Sci Upper Austria Sch Informat Commun & Media Heurist & Evolutionary Algorithms Lab Softwarepk 11 A-4232 Hagenberg Austria Johannes Kepler Univ Linz Dept Knowledge Based Math Syst Altenbergerstr 49 A-4040 Linz Austria Miba Frictec Peter Mitterbauer Str A-4661 Roitham Austria

Tribological systems are mechanical systems that rely on friction to transmit forces. The design and dimensioning of such systems requires prediction of various characteristic, such as the coefficient of friction. The core contribution of this paper is the analysis of two data-based modeling techniques which can be used to produce accurate and at the same time interpretable models for friction systems. We focus on two methods for building interpretable and potentially non-linear regression models: (i) robust fuzzy modeling with batch processing and an enhanced regularized learning scheme, and (ii) enhanced symbolic regression using genetic programming. We compare our results of both methods with state-of-the-art methods and found that linear models are insufficient for predicting the coefficient of friction, temperature, wear, and noise-vibration-harshness rating of the tribological systems, while the proposed robust fuzzy modeling and the enhanced symbolic regression approaches, as well as the state-of-the-art regression techniques, are able to generate satisfactory models. However, robust fuzzy modeling and enhanced symbolic regression lead to simpler models with fewer parameters that can be interpreted by domain experts. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Tribological systems Robust fuzzy modeling Generalized Takagi-Sugeno fuzzy systems Symbolic regression multi-objective genetic programming

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A hybrid model for predicting human physical activity status from lifelogging data

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2020年第3期281卷 532-542页

作者： Ni, Ji Chen, Bowei Allinson, Nigel M. Ye, Xujiong Univ Lincoln Sch Comp Sci Lincoln England Univ Glasgow Adam Smith Business Sch Glasgow Lanark Scotland Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

One trend in the recent healthcare transformations is people are encouraged to monitor and manage their health based on their daily diets and physical activity habits. However, much attention of the use of operational research and analytical models in healthcare has been paid to the systematic level such as country or regional policy making or organisational issues. This paper proposes a model concerned with healthcare analytics at the individual level, which can predict human physical activity status from sequential lifelogging data collected from wearable sensors. The model has a two-stage hybrid structure (in short, MOGP-HMM) - a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model (HMM) in the second stage for activity status prediction over time. It can be used as a decision support tool to provide real-time monitoring, statistical analysis and personalized advice to individuals, encouraging positive attitudes towards healthy lifestyles. We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models. Our experimental results show that the MOGP-HMM can achieve comparable performance. To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals' activity status prediction. It fits seamlessly with the current trend in the UK healthcare transformation of patient empowerment as well as contributing to a strategic development for more efficient and cost-effective provision of healthcare. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Machine learning Physical activity status prediction multi-objective genetic programming Hidden Markov model

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An Integrated Approach to Stage 1 Breast Cancer Detection 15

An Integrated Approach to Stage 1 Breast Cancer Detection

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17th genetic and Evolutionary Computation Conference (GECCO)

作者： Fitzgerald, Jeannie M. Ryan, Conor Medernach, David Krawiec, Krzysztof Univ Limerick Limerick Ireland Poznan Univ Tech Poznan Poland

ISBN: (纸本)9781450334723

We present an automated, end-to-end approach for Stage 1 breast cancer detection. The first phase of our proposed work-flow takes individual digital mammograms as input and outputs several smaller sub-images from which the background has been removed. Next, we extract a set of features which capture textural information from the segmented images. In the final phase, the most salient of these features are fed into a multi-objective genetic programming system which then evolves classifiers capable of identifying those segments which may have suspicious areas that require further investigation. A key aspect of this work is the examination of several new experimental configurations which focus on textural asymmetry between breasts. The best evolved classifier using such a configuration can deliver results of 100% accuracy on true positives and a false positive per image rating of just 0.33, which is better than the current state of the art.

关键词： Mammography Classification multi-objective genetic programming

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