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multi-objective genetic programming for nonlinear system identification

ELECTRONICS LETTERS 1998年第9期34卷 930-931页

作者： Rodriguez-Vazquez, K Fleming, PJ Univ Sheffield Sheffield S1 3JD S Yorkshire England

genetic programming is applied to the identification of non-linear polynomial models. This approach optimises multiple objectives simultaneously, and the solution set provides a trade-off between the complexity and the performance of the models. This is achieved using the concept of the non-dominated or Pareto-optimal solutions. The approach is tested on the simple Wiener model.

关键词： Wiener model nondominated solution Simulation, modelling and identification Optimisation techniques nonlinear systems genetic algorithms polynomials multi-objective genetic programming Pareto-optimal solution nonlinear system identification identification polynomial model

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multi-objective genetic programming strategies for topic-based search with a focus on diversity and global recall

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1710页

作者： Baggio, Cecilia Lorenzetti, Carlos M. Cecchini, Rocio L. Maguitman, Ana G. Inst Ciencias & Ingn Comp UNS CONICET Bahia Blanca Buenos Aires Argentina Univ Nacl Del Sur Dept Ciencias & Ingn Comp Bahia Blanca Buenos Aires Argentina

Topic-based search systems retrieve items by contextualizing the information seeking process on a topic of interest to the user. A key issue in topic-based search of text resources is how to automatically generate multiple queries that reflect the topic of interest in such a way that precision, recall, and diversity are achieved. The problem of generating topic-based queries can be effectively addressed by multi-objective Evolutionary Algorithms, which have shown promising results. However, two common problems with such an approach are loss of diversity and low global recall when combining results from multiple queries. This work proposes a family of multi objective genetic programming strategies based on objective functions that attempt to maximize precision and recall while minimizing the similarity among the retrieved results. To this end, we define three novel objective functions based on result set similarity and on the information theoretic notion of entropy. Extensive experiments allow us to conclude that while the proposed strategies significantly improve precision after a few generations, only some of them are able to maintain or improve global recall. A comparative analysis against previous strategies based on multi objective Evolutionary Algorithms, indicates that the proposed approach is superior in terms of precision and global recall. Furthermore, when compared to query-term selection methods based on existing state-of-the-art term-weighting schemes, the presented multi-objective genetic programming strategies demonstrate significantly higher levels of precision, recall, and F1-score, while maintaining competitive global recall. Finally, we identify the strengths and limitations of the strategies and conclude that the choice of objectives to be maximized or minimized should be guided by the application at hand.

关键词： multi-objective genetic programming Information retrieval Topic-based search Diversity preservation Global recall Automatic query formulation Diversity maximization Learning complex queries Information-theoretic fitness functions

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multi-class pattern classification using single, multi-dimensional feature-space feature extraction evolved by multi-objective genetic programming and its application to network intrusion detection

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genetic programming AND EVOLVABLE MACHINES 2012年第1期13卷 33-63页

作者： Badran, Khaled Rockett, Peter Univ Sheffield Dept Elect & Elect Engn Vis & Informat Engn Res Grp Sheffield S1 3D S Yorkshire England

In this paper we investigate using multi-objective genetic programming to evolve a feature extraction stage for multiple-class classifiers. We find mappings which transform the input space into a new, multi-dimensional decision space to increase the discrimination between all classes;the number of dimensions of this decision space is optimized as part of the evolutionary process. A simple and fast multi-class classifier is then implemented in this multi-dimensional decision space. Mapping to a single decision space has significant computational advantages compared to k-class-to-2-class decompositions;a key design requirement in this work has been the ability to incorporate changing priors and/or costs associated with mislabeling without retraining. We have employed multi-objective optimization in a Pareto framework incorporating solution complexity as an independent objective to be minimized in addition to the main objective of the misclassification error. We thus give preference to simpler solutions which tend to generalize well on unseen data, in accordance with Occam's Razor. We obtain classification results on a series of benchmark problems which are essentially identical to previous, more complex decomposition approaches. Our solutions are much simpler and computationally attractive as well as able to readily incorporate changing priors/costs. In addition, we have also applied our approach to the KDD-99 intrusion detection dataset and obtained results which are highly competitive with the KDD-99 Cup winner but with a significantly simpler classification framework.

关键词： multi-class pattern classification Feature extraction Feature selection multi-objective genetic programming

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Evolutionary convolutional neural network for image classification based on multi-objective genetic programming with leader-follower mechanism

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COMPLEX & INTELLIGENT SYSTEMS 2023年第3期9卷 3211-3228页

作者： Liu, Qingqing Wang, Xianpeng Wang, Yao Song, Xiangman Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Op Shenyang 110819 Peoples R China Liaoning Engn Lab Data Analyt & Optimizat Smart I Shenyang 110819 Peoples R China Northeastern Univ Key Lab Data Analyt & Optimizat Smart Ind Minist Educ Shenyang 110819 Peoples R China

As a popular research in the field of artificial intelligence in the last 2 years, evolutionary neural architecture search (ENAS) compensates the disadvantage that the construction of convolutional neural network (CNN) relies heavily on the prior knowledge of designers. Since its inception, a great deal of researches have been devoted to improving its associated theories, giving rise to many related algorithms with pretty good results. Considering that there are still some limitations in the existing algorithms, such as the fixed depth or width of the network, the pursuit of accuracy at the expense of computational resources, and the tendency to fall into local optimization. In this article, a multi-objective genetic programming algorithm with a leader-follower evolution mechanism (LF-MOGP) is proposed, where a flexible encoding strategy with variable length and width based on Cartesian genetic programming is designed to represent the topology of CNNs. Furthermore, the leader-follower evolution mechanism is proposed to guide the evolution of the algorithm, with the external archive set composed of non-dominated solutions acting as the leader and an elite population updated followed by the external archive acting as the follower. Which increases the speed of population convergence, guarantees the diversity of individuals, and greatly reduces the computational resources. The proposed LF-MOGP algorithm is evaluated on eight widely used image classification tasks and a real industrial task. Experimental results show that the proposed LF-MOGP is comparative with or even superior to 35 existing algorithms (including some state-of-the-art algorithms) in terms of classification error and number of parameters.

关键词： Evolutionary neural architecture search multi-objective genetic programming Leader-follower mechanism

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

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

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genetic and Evolutionary Computation Conference

作者： Zhang, Yang Rockett, Peter I. Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)1595930108

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 feature extractor multi-objective genetic programming

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Promoting Semantic Diversity in multi-objective genetic programming

Promoting Semantic Diversity in Multi-objective Genetic Prog...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Galvan, Edgar Schoenauer, Marc Maynooth Univ Dept Comp Sci Maynooth Kildare Ireland Univ Paris Saclay INRIA TAU Paris France Univ Paris Saclay CNRS Paris France Univ Paris Saclay Univ Paris Sud Paris France

ISBN: (纸本)9781450361118

The study of semantics in genetic programming (GP) has increased dramatically over the last years due to the fact that researchers tend to report a performance increase in GP when semantic diversity is promoted. However, the adoption of semantics in Evolutionary multi-objective Optimisation (EMO), at large, and in multi-objective GP (MOGP), in particular, has been very limited and this paper intends to fill this challenging research area. We propose a mechanism wherein a semantic-based distance is used instead of the widely known crowding distance and is also used as an objective to be optimised. To this end, we use two well-known EMO algorithms: NSGA-II and SPEA2. Results on highly unbalanced binary classification tasks indicate that the proposed approach produces more and better results than the rest of the three other approaches used in this work, including the canonical aforementioned EMO algorithms.

关键词： multi-objective genetic programming Semantics

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Automatic design of scheduling policies for dynamic flexible job shop scheduling by multi-objective genetic programming based hyper-heuristic 12

Automatic design of scheduling policies for dynamic flexible...

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12th CIRP Conference on Intelligent Computation in Manufacturing Engineering (CIRP ICME)

作者： Zhou, Yong Yang, Jian-jun Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R 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. (C) 2019 The Authors. Published by Elsevier B. V.

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

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Highlights of Semantics in multi-objective genetic programming 22

Highlights of Semantics in Multi-objective Genetic Programmi...

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genetic and Evolutionary Computation Conference (GECCO)

作者： Galvan, Edgar Trujillo, Leonardo Stapleton, Fergal Maynooth Univ Dept CS Hamilton Inst IVILeroNat Inspired Comp Res Grp Maynooth Kildare Ireland Tecnol Nacl Mexico IT Tijuana Tijuana BC Mexico Maynooth Univ Dept CS Hamilton Inst Nat Inspired Comp Res Grp Maynooth Kildare Ireland

ISBN: (纸本)9781450392686

Semantics is a growing area of research in genetic programming (GP) and refers to the behavioural output of a genetic programming individual when executed. This research expands upon the current understanding of semantics by proposing a new approach: Semantic-based Distance as an additional criteriOn (SDO), in the thus far, somewhat limited researched area of semantics in multio-bjective GP (MOGP). Our work included an expansive analysis of the GP in terms of performance and diversity metrics, using two additional semantic-based approaches, namely Semantic Similarity-based Crossover (SCC) and Semantic-based Crowding Distance (SCD). Each approach is integrated into two evolutionary multi-objective (EMO) frameworks: Non-dominated Sorting genetic Algorithm II (NSGA-II) and the Strength Pareto Evolutionary Algorithm 2 (SPEA2), and along with the three semantic approaches, the canonical form of NSGA-II and SPEA2 are rigorously compared. Using highly-unbalanced binary classification datasets, we demonstrated that the newly proposed approach of SDO consistently generated more non-dominated solutions, with better diversity and improved hypervolume results.

关键词： multi-objective genetic programming Semantics Diversity

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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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5th International Conference on Machine Learning and Cybernetics

作者： Zhang, Yang Univ Sheffield Dept Elect & Elect Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)1424400600

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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Semantics in multi-objective genetic programming

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APPLIED SOFT COMPUTING 2022年第0期115卷 108143-108143页

作者： Galvan, Edgar Trujillo, Leonardo Stapleton, Fergal Natl Univ Ireland Maynooth Hamilton Inst Dept Comp Sci Naturally Inspired Computat Res Grp Lero Ireland Tecnol Nacl Mexico IT Tijuana Tijuana BC Mexico

Semantics has become a key topic of research in genetic programming (GP). Semantics refers to the outputs (behaviour) of a GP individual when this is run on a dataset. The majority of works that focus on semantic diversity in single-objective GP indicates that it is highly beneficial in evolutionary search. Surprisingly, there is minuscule research conducted in semantics in multi-objective GP (MOGP). In this work we make a leap beyond our understanding of semantics in MOGP and propose SDO: Semantic-based Distance as an additional criteriOn. This naturally encourages semantic diversity in MOGP. To do so, we find a pivot in the less dense region of the first Pareto front (most promising front). This is then used to compute a distance between the pivot and every individual in the population. The resulting distance is then used as an additional criterion to be optimised to favour semantic diversity. We also use two other semantic-based methods as baselines, called Semantic Similarity-based Crossover and Semantic-based Crowding Distance. Furthermore, we also use the Non-dominated Sorting genetic Algorithm II and the Strength Pareto Evolutionary Algorithm 2 for comparison too. We use highly unbalanced binary classification problems and consistently show how our proposed SDO approach produces more non-dominated solutions and better diversity, leading to better statistically significant results, using the hypervolume results as evaluation measure, compared to the rest of the other four methods. (C) 2021 Elsevier B.V. All rights reserved.

关键词： multi-objective genetic programming Semantics Diversity NSGA-II SPEA2

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