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Constant optimization and feature standardization in multiobjective genetic programming

genetic programming AND EVOLVABLE MACHINES 2022年第1期23卷 37-69页

作者： Rockett, Peter Univ Sheffield Dept Elect & Elect Engn Mappin St Sheffield S1 3JD S Yorkshire England

This paper extends the numerical tuning of tree constants in genetic programming (GP) to the multiobjective domain. Using ten real-world benchmark regression datasets and employing Bayesian comparison procedures, we first consider the effects of feature standardization (without constant tuning) and conclude that standardization generally produces lower test errors, but, contrary to other recently published work, we find much less clear trend for tree sizes. In addition, we consider the effects of constant tuning - with and without feature standardization - and observe that (1) constant tuning invariably improves test error, and (2) usually decreases tree size. Combined with standardization, constant tuning produces the best test error results;tree sizes, however, are increased. We also examine the effects of applying constant tuning only once at the end a conventional GP run which turns out to be surprisingly promising. Finally, we consider the merits of using numerical procedures to tune tree constants and observe that for around half the datasets evolutionary search alone is superior whereas for the remaining half, parameter tuning is superior. We identify a number of open research questions that arise from this work.

关键词： multiobjective genetic programming Constant optimization Feature standardization Bayesian testing

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Evolutionary modeling approach based on multiobjective genetic programming for strip quality prediction

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Wang, Yao Wang, Xianpeng Tang, Lixin Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Shenyang 110819 Peoples R China Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang 110819 Peoples R China Liaoning Engn Lab Data Analyt & Optimizat Smart In Shenyang 110819 Peoples R China

In the iron and steel industry, hardness is one of the key indicators of strip quality in the continuous annealing production line (CAPL). However, the complex production process and the strong coupled nonlinearity between process parameters make it difficult to develop accurate mechanism models and pose a challenge for datadriven modeling approaches. More importantly, most of the data -driven learning methods lack interpretability and cannot characterize the mathematical relationship between process parameters and product quality, which in turn makes it extremely hard to understand the process mechanism. Therefore, this paper proposes an interpretable modeling approach (IMA) based on feature decomposition and ensemble to construct interpretable analytical models between process parameters and strip quality. In the IMA, a data -mechanism fusionbased feature decomposition (DM_FD) method is first applied to cope with high -dimensional input feature problems. Then, an improved multiobjective genetic programming algorithm (iMOGP) is developed to construct interpretability sub -models. Finally, a sparse optimization ensemble method (SOE) is used to integrate the sub -models to achieve interpretability and good generalization. Experimental results based on practical strip data demonstrate that the proposed IMA can cope well with high -dimensional input features and achieve model interpretability compared with commonly used machine learning methods and genetic programming (GP) -based modeling methods while ensuring better accuracy and generalization.

关键词： Interpretable modeling Product quality prediction Data-mechanism fusion multiobjective genetic programming Continuous annealing

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multiobjective genetic programming for reinforced concrete beam modeling

Applied AI Letters

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Applied AI Letters 2020年第1期1卷

作者： Amirhessam Tahmassebi Behshad Mohebali Anke Meyer-Baese Amir H. Gandomi Department of Scientific Computing Florida State University Tallahassee Florida USA Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

This paper presents the application of multiobjective genetic programming (MOGP) in engineering issues. An evolutionary symbolic implementation was developed based on a case study on prediction of the shear strength of slender reinforced concrete beams without stirrups including 1942 set of published test results. In the implementation of the MOGP model, the nondominated sorting genetic algorithm II with adaptive regression by mixing algorithm with considering the optimization of mean-square error as the fitness measure and the subtree complexity was used. The developed MOGP model was compared to previously developed genetic programming models, different building codes, and additional machine learning based approaches. It is clearly shown that the MOGP model outperformed the other algorithms applied on this database and can be a general solution on any engineering problems with the main advantage of prediction equations without assuming prior form of the relevance among the input predictor variables.

关键词： multiobjective genetic programming RC beam symbolic regression

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Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective genetic programming

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 86卷 1-10页

作者： Echevarria, Yuviny Blanco, Cecilio Sanchez, Luciano Univ Oviedo Dept Informat Oviedo Spain Univ Oviedo Dept Elect Oviedo Spain

The health of automotive Li-ion batteries depends on different side reactions on the electrodes that may degrade the cells, thereby reducing their useable capacity and sometimes producing catastrophic failures with serious economic and safety implications. In this paper, a method of detection and prognosis of battery deterioration is proposed in which an intelligent soft sensor is able to synthesize human-understandable health indicators from sequences of voltages, currents and temperatures streamed via on-vehicle sensors. This soft sensor is based on a dynamic model optimizing three different criteria obtained by means of multi-objective grammatical evolution. Different survival selection strategies suitable for this problem are discussed and compared.

关键词： multiobjective genetic programming Grammatical evolution Battery model

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The influence of mutation on population dynamics in multiobjective genetic programming

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genetic programming AND EVOLVABLE MACHINES 2010年第1期11卷 5-33页

作者： Badran, Khaled Rockett, Peter I. Univ Sheffield Dept Elect & Elect Engn Lab Image & Vis Engn Sheffield S1 3JD S Yorkshire England

Using multiobjective genetic programming with a complexity objective to overcome tree bloat is usually very successful but can sometimes lead to undesirable collapse of the population to all single-node trees. In this paper we report a detailed examination of why and when collapse occurs. We have used different types of crossover and mutation operators (depth-fair and sub-tree), different evolutionary approaches (generational and steady-state), and different datasets (6-parity Boolean and a range of benchmark machine learning problems) to strengthen our conclusion. We conclude that mutation has a vital role in preventing population collapse by counterbalancing parsimony pressure and preserving population diversity. Also, mutation controls the size of the generated individuals which tends to dominate the time needed for fitness evaluation and therefore the whole evolutionary process. Further, the average size of the individuals in a GP population depends on the evolutionary approach employed. We also demonstrate that mutation has a wider role than merely culling single-node individuals from the population;even within a diversity-preserving algorithm such as SPEA2 mutation has a role in preserving diversity.

关键词： multiobjective genetic programming Population collapse Mutation Population dynamics

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Modeling a complex system using multiobjective genetic programming

Modeling a complex system using multiobjective genetic progr...

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3rd IEEE Conference on Industrial Electronics and Applications (ICIEA 2008)

作者： Bai Yan Jiang Yiheng Zhu Yaochun Li Xiaofei Fan Li N China Elect Power Univ Dept Automat Beijing 102206 Peoples R China China Natl Water Resources & Elect Power Mat & Eq Beijing 100045 Peoples R China Zhong Xing Power Ind & Commercial Dev Co Ltd Beijing 100053 Peoples R China

ISBN: (纸本)9781424417186

genetic programming is a kind of searching technique, which is intelligent, and it works by emulating natural evolution. The intelligent modeling technique based on genetic programming can recognize the relationship between the inputs and outputs of a complex system, and it has ascendeny of searching out the frame and the parameters of the system at the same time and the searching results are explicit. A novel multiobjective genetic programming, which searching aim is to minimize the sum of squares of deviations, the complexity and the maximal dynamic deviation, is put forward to model a coordinate system of power plant, and the results from simulation show that it is valid.

关键词： multiobjective genetic programming transform function system modeling the coordinate system of power plant

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Evolving a Conspicuous Point Detector based on an Artificial Dorsal Stream - SLAM System 12

Evolving a Conspicuous Point Detector based on an Artificial...

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

作者： Hernandez, Daniel Olague, Gustavo Clemente, Eddie Dozal, Leon CICESE Div Appl Phys Dept Comp Sci EvoVis Project Mexico City DF Mexico

ISBN: (纸本)9781450311779

The goal of purposive or behavioral vision is to study the interactions of a visual system with the real world, creating a balance between perception and action. It is said that a system that accomplishes a visuomotor task needs to implement a selective perception process allowing specific motion-action commands. This combination is understood as a visual behavior. This paper describes a real-working system, consisting of a robotic manipulator in a hand-eye configuration, which is used as a research platform in order to evolve a specialised visual routine capable of estimating specific motion-actions. The core idea is to evolve a conspicuous point detector, based on the artificial dorsal stream model, with the purpose of using this detector inside a simultaneous localization and map building system. Experimental results show as a proof-of-concept several interesting ideas;first, that it is in fact possible to find prominent points in an image through a visual attention process;and second, that the proposed system is able to design specific visual behaviors.

关键词： Evolutionary Visual Behavior multiobjective genetic programming Purposive Vision Organic GP SLAM

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Multi-Objective genetic programming for Object Detection

Multi-Objective Genetic Programming for Object Detection

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2010 IEEE World Congress on Computational Intelligence

作者： Liddle, Thomas Johnston, Mark Zhang, Mengjie Victoria Univ Wellington Sch Math Stat & Operat Res POB 600 Wellington New Zealand Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781424481262

In object detection, the goals of successfully discriminating between different kinds of objects (object classification) and accurately identifying the positions of all objects of interest in a large image (object localisation) are potentially in conflict. We propose a Multi-Objective genetic programming (MOGP) approach to the task of providing a decision-maker with a diverse set of alternative object detection programs that balance between high detection rate and low false-alarm rate. Experiments on two datasets, simple shapes and photographs of coins, show that it is difficult for a Single-Objective GP (SOGP) system (which weights the multiple objectives a priori) to evolve effective object detectors, but that an MOGP system is able to evolve a range of effective object detectors more efficiently.

关键词： alternative object detection programs coin photographs decision-maker genetic algorithms large image multiobjective genetic programming object classification object detection object localisation

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A Comparison of the Generalization Ability of Different genetic programming Frameworks

A Comparison of the Generalization Ability of Different Gene...

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2010 IEEE World Congress on Computational Intelligence

作者： Castelli, Mauro Manzoni, Luca Silva, Sara Vanneschi, Leonardo Univ Milano Bicocca Dept Informat Syst & Commun Viale Sarca 336 I-20126 Milan Italy INESC KDBIO Grp P-1000 Lisbon Portugal Univ Coimbra Ctr Informat Syst ECOS Grp P-3000 Coimbra Portugal

ISBN: (纸本)9781424481262

Generalization is an important issue in machine learning. In fact, in several applications good results over training data are not as important as good results over unseen data. While this problem was deeply studied in other machine learning techniques, it has become an important issue for genetic programming only in the last few years. In this paper we compare the generalization ability of several different genetic programming frameworks, including some variants of multi-objective genetic programming and operator equalization, a recently defined bloat free genetic programming system. The test problem used is a hard regression real-life application in the field of drug discovery and development, characterized by a high number of features and where the generalization ability of the proposed solutions is a crucial issue. The results we obtained show that, at least for the considered problem, multi-optimization is effective in improving genetic programming generalization ability, outperforming all the other methods on test data.

关键词： drug development drug discovery generalisation (artificial intelligence) generalization ability genetic algorithms genetic programming framework hard regression real-life application learning (artificial intelligence) machine learning multiobjective genetic programming operator equalization pharmaceutical industry regression analysis

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A multiobjective genetic programming approach for pricing and hedging derivative securities

A multiobjective genetic programming approach for pricing an...

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IEEE International Conference on Computational Intelligence for Financial Engineering

作者： Schuster, MG Univ Vienna Dept Business Adm A-1210 Vienna Austria

ISBN: (纸本)0780376544

genetic programming has become increasingly important in the broad field of finance. Due to the fact that in the context of pricing derivatives a lot of models are not amenable to analytical exact solutions, this is especially true for the research area of contingent claim pricing. Previous work in this field almost certainly always focuses on pricing such derivatives. In doing so, the implied hedging performance is totally ignored due to the lack of an analysis of price sensitivities which are fundamental building-blocks in hedging-strategies. In this contribution we apply a multiobjective genetic programming approach to the American put pricing problem and evaluate our individuals based on the pricing as well as on the hedging performance by means of symbolic differentiation.

关键词： multiobjective genetic programming option pricing hedging

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