An analysis of accuracy-diversity trade-off for hybrid combined system with multiobjective predictor selection

Tsakonas, Athanasios

doi:10.1007/s10489-013-0507-8

Cited by 5 publications

(7 citation statements)

References 37 publications

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“…The two objectives may conflict with each other, meaning that to improve one objective may weaken the other. There is no such a solution which is optimized in both objectives [33][34][35][36]. The optimal solution set for the bi-objective optimization problem is known as the Pareto-optimal set.…”

Section: Discussionmentioning

confidence: 99%

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

Xing

Bai

et al. 2014

Appl Intell

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract:In telecommunications networks, to enable a valid data transmission based on network coding, any intermediate node within a given network is allowed, if necessary, to perform coding operations. The more coding operations needed, the more coding resources consumed and thus the more computational overhead and transmission delay incurred. This paper investigates an efficient evolutionary algorithm to minimize the amount of coding operations required in network coding based multicast. Based on genetic algorithms, we adapt two extensions in the proposed evolutionary algorithm, namely a new crossover operator and a neighbourhood search operator, to effectively solve the highly complex problem being concerned.The new crossover is based on logic OR operations to each pair of selected parent individuals and the resulting offspring are more likely to become feasible. The aim of this operator is to intensify the search in regions with plenty of feasible individuals. The neighbourhood search consists of two moves which are based on greedy link removal and path reconstruction, respectively. Due to the specific problem feature, it is possible that each feasible individual corresponds to a number of, rather than a single, valid network coding based routing subgraphs. The neighbourhood search is applied to each feasible individual to find a fitter routing subgraph that consumes less coding resource. This operator not only improves solution quality but also accelerates the convergence.Experiments have been carried out on a number of fixed and randomly generated benchmark networks. The results demonstrate that with the two extensions, our evolutionary algorithm is effective and outperforms a number of state-of-the-art algorithms in terms of the ability of finding optimal solutions.

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Section: Discussionmentioning

confidence: 99%

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

Xing

Bai

et al. 2014

Appl Intell

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“…One of the most popular EA-based nonlinear methods are expression trees (Escalante et al, 2013;Tsakonas, 2014;Liu et al, 2014aLiu et al, , 2015Lacy et al, 2015b,a;Folino et al, 2016). Expression trees have models in their leaves and combination operators in their inner nodes.…”

Section: Expression Treesmentioning

confidence: 99%

A survey of evolutionary algorithms for supervised ensemble learning

Cagnini

Dôres

Freitas

et al. 2023

The Knowledge Engineering Review

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This paper presents a comprehensive review of evolutionary algorithms that learn an ensemble of predictive models for supervised machine learning (classification and regression). We propose a detailed four-level taxonomy of studies in this area. The first level of the taxonomy categorizes studies based on which stage of the ensemble learning process is addressed by the evolutionary algorithm: the generation of base models, model selection, or the integration of outputs. The next three levels of the taxonomy further categorize studies based on methods used to address each stage. In addition, we categorize studies according to the main types of objectives optimized by the evolutionary algorithm, the type of base learner used and the type of evolutionary algorithm used. We also discuss controversial topics, like the pros and cons of the selection stage of ensemble learning, and the need for using a diversity measure for the ensemble’s members in the fitness function. Finally, as conclusions, we summarize our findings about patterns in the frequency of use of different methods and suggest several new research directions for evolutionary ensemble learning.

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“…Its modularity has been widely used to represent restrictions on general domains describing constraints and interactions within systems (O'Neill and Ryan, 2001;Dempsey et al, 2007;Brabazon et al, 2008). There are also studies in which GE has been applied to learn linguistically understandable rules from data (Garcia et al, 1999), although some authors claim that GPBL may be preferred to rule-based learning systems in terms of the interpretability of the extracted knowledge in many applications (Tsakonas, 2014). GE is often criticized because of the disruptiveness of grammar-based crossover operators, while many authors have studied the definition of operators that make small changes in the genotype, thus avoiding large changes in the phenotype (McKay et al, 2010;Vanneschi et al, 2014;Fenton et al, 2014;Lourenço et al, 2016).…”

Section: Multi-objective Genetic Programming-based Learning Modelsmentioning

confidence: 99%

“…The diversity in the population for grammar-driven MOGP and symbolic regression was studied by Tsakonas (Tsakonas, 2014). Rezaee (Rezaee et al, 2013) minimized two different objectives in the optimization of Quantum-Dot Cellular Automata electronic circuits.…”

Section: Evolution Control: Dominance and Elitismmentioning

confidence: 99%

“…This "injected" knowledge can comprise linguistic rules (Fazzolari et al, 2013), mathematical expressions (Kozek and Sinanović, 2008;Greblicki, 1992), semi-physical analogies (Blanco et al, 2014) or many others. Fuzzy Rule-Based Systems (FRBS) arguably constitute the most popular mechanism for integrating data and expert knowledge, although in this paper we propose that Genetic Programming-Based Learning (GPBL) is more flexible (Tsakonas, 2014), as GPBL expressions include FRBS as a particular case. FRBS are valid chains in a context-free grammar and GPBL algorithms for FRBS exist (Sanchez et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Echevarría

Blanco

Sánchez

2019

Engineering Applications of Artificial Intelligence

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An analysis of accuracy-diversity trade-off for hybrid combined system with multiobjective predictor selection

Cited by 5 publications

References 37 publications

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

A survey of evolutionary algorithms for supervised ensemble learning

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

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