Incremental learning of collaborative classifier agents with new class acquisition: An incremental genetic algorithm approach

Guan, Sheng; Zhu, Fayan

doi:10.1002/int.10145

Cited by 12 publications

(5 citation statements)

References 16 publications

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“…Incremental learning 12 is a popular technique that refers to learning controller behaviours by progressively increasing the complexity and/or scope of the learning task. It has been studied with several multi-agent domains [13][14][15][16] and has demonstrated successful results in comparison to direct evolution. Curriculum learning 17 is a relatively recent idea, which is an extension of incremental learning and is inspired by the curriculum and organisation of the education system which progressively introduces different concepts and examples.…”

Section: Abstraction Learning Architecturesmentioning

confidence: 99%

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

Samarasinghe

Barlow

Lakshika

et al. 2021

Int J of Intelligent Sys

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This paper presents a grammar‐based evolutionary approach that incorporates abstractions to learn complex collective behaviours through their simpler representations. We propose modifications to the grammar syntax design and genome structure to facilitate evolution of abstractions in separate genome partitions. Two abstraction techniques based on behavioural decomposition and environmental scaffolding are presented to derive these simpler representations. Parallel and incremental learning architectures incorporated with grammatical evolution (GE) are investigated with three complex problems to evaluate their potential in generating collective multi‐agent behaviours. The results infer that both learning architectures surpass a generic GE model in performance for evolving complex behaviours. Furthermore, using environmental scaffolding reduces the robustness of the model than when only the behavioural decomposition technique is used. However, it has more potential to generate solutions with better fitness than when scaffoldings are not used. The evaluations suggest that, by incorporating abstraction learning architectures with grammar‐based evolution can significantly improve the performance of an agent system in complex problem domains.

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Section: Abstraction Learning Architecturesmentioning

confidence: 99%

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

Samarasinghe

Barlow

Lakshika

et al. 2021

Int J of Intelligent Sys

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“…13,14 It is developed on the basis of a normal GA. Figure 1 illustrates the pseudocode of IGA. When a new attribute is ready to be integrated, an initial population is formed by integrating the old chromosomes (solutions) and new elements for the new attribute if available.…”

Section: Incremental Genetic Algorithm~iga!mentioning

confidence: 99%

“…This article uses the incremental genetic algorithm (IGA) as a basic evolutionary algorithm for incremental learning, 13,14 and proposes ordered incremental genetic algorithms (OIGAs) for the incremental training of input attributes for classifiers. Different from normal GAs, which learn input attributes in their full dimension, OIGAs learn the attributes one after another under a situation of continuous incremental learning.…”

Section: Introductionmentioning

confidence: 99%

Ordered incremental training with genetic algorithms

Zhu

Guan

2004

Int. J. Intell. Syst.

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Incremental training has been used for genetic algorithm (GA)-based classifiers in a dynamic environment where training samples or new attributes/classes become available over time. In this article, ordered incremental genetic algorithms (OIGAs) are proposed to address the incremental training of input attributes for classifiers. Rather than learning input attributes in batch as with normal GAs, OIGAs learn input attributes one after another. The resulting classification rule sets are also evolved incrementally to accommodate the new attributes. Furthermore, attributes are arranged in different orders by evaluating their individual discriminating ability. By experimenting with different attribute orders, different approaches of OIGAs are evaluated using four benchmark classification data sets. Their performance is also compared with normal GAs. The simulation results show that OIGAs can achieve generally better performance than normal GAs. The order of attributes does have an effect on the final classifier performance where OIGA training with a descending order of attributes performs the best.

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“…For example, based on machine learning datasets from UCI, Guan et al employed IAL to solve several classi¯cation and regression problems by neural networks and genetic algorithms. [1][2][3][4][5][6][7][8][9][10][11][12][13][14] Almost all of their results using IAL were better than those derived from traditional methods. Speci¯cally, based on Incremental Learning in terms of Input Attributes (ILIA) 1 and ITID, two e®ective algorithms were developed on the basis of IAL, and as a result, classi¯cation errors obtained by incremental neural networks for input feature learning of Diabetes, Thyroid and Glass datasets reduced by 8.2%, 14.6% and 12.6% respectively.…”

Section: A Brief Introduction To Ialmentioning

confidence: 97%

“…This strategy makes the features with greater discrimination abilities be trained in an earlier step than others, and get rid of interference between features during classication. Previous studies showed that IAL can be independently employed and successfully applied based on many machine learning approaches, such as neural networks (NN), 1-8 genetic algorithms (GA), [9][10][11][12][13][14] decision trees (DT), 15 support vector machines (SVM), 16 and Bayesian classi¯er. 17 Apart from this,¯nal classi¯cation results produced by IAL also exhibit better performance than those derived by conventional one-batch training approaches.…”

Section: Introductionmentioning

confidence: 99%

Optimized Neural Incremental Attribute Learning for Classification Based on Statistical Discriminability

Wang

Guan

Man

et al. 2014

Int. J. Comp. Intel. Appl.

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Feature ordering is a signi¯cant data preprocessing method in incremental attribute learning (IAL), where features are gradually trained according to a given order. Previous research showed feature ordering is crucial to the IAL performance. It is relevant to each feature's discrimination ability, which can be calculated by single discriminability (SD). However, when feature dimensions increase, feature discrimination ability should also be calculated incrementally, because discrimination ability in lower dimensional spaces is di®erent from that in higher spaces. Thus based on SD, accumulative discriminability (AD), a new statistical metric for incremental feature discrimination ability estimation, is designed. Moreover, a criterion that summarizes all the produced values of AD is employed to obtain the optimum feature ordering for classi¯cation problems based on neural networks by means of IAL. In addition, in order to reduce the time consumption, an e®ective feature ordering approach is developed. Compared with the feature ordering obtained by other approaches, the method outlined in this paper obtained good¯nal classi¯cation results, which indicates that,¯rstly, feature discrimination ability should be incrementally estimated in IAL; and secondly, feature ordering derived by AD and its corresponding approaches are applicable with IAL.

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Incremental learning of collaborative classifier agents with new class acquisition: An incremental genetic algorithm approach

Cited by 12 publications

References 16 publications

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

Exploiting abstractions for grammar‐based learning of complex multi‐agent behaviours

Ordered incremental training with genetic algorithms

Optimized Neural Incremental Attribute Learning for Classification Based on Statistical Discriminability

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