Multi-objective memetic evolution of ART-based classifiers

Li, Rong; Mersch, Timothy R.; Wen, Oriana X.; Kaylani, Assem; Anagnostopoulos, Georgios C.

doi:10.1109/cec.2010.5586385

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…are not well suited for such problems. The only approach in literature that is aimed at generating and evolving a population of FAM networks that are diverse in term of structural complexity, yet contained non-dominated alternatives, is presented in (Li et al, 2010). In this case, a memetic archive was instead used to prune F 2 layer nodes and categorize FAM networks into subpopulations that evolved independently according to some genetic algorithm.…”

Section: Ensembles and Dynamic Moomentioning

confidence: 99%

Evolving Classifier Ensembles using Dynamic Multi-objective Swarm Intelligence

Connolly¹,

Granger²,

Sabourin³

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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Classification systems are often designed using a limited amount of data from complex and changing pattern recognition environments. In applications where new reference samples become available over time, adaptive multi-classifier systems (AMCSs) are desirable for updating class models. In this paper, an incremental learning strategy based on an aggregated dynamical niching particle swarm optimization (ADNPSO) algorithm is proposed to efficiently evolve heterogeneous classifier ensembles in response to new reference data. This strategy is applied to an AMCS where all parameters of a pool of fuzzy ARTMAP (FAM) neural network classifiers, each one corresponding to a PSO particle, are co-optimized such that both error rate and network size are minimized. To sustain a high level of accuracy while minimizing the computational complexity, the AMCS integrates information from multiple diverse classifiers, where learning is guided by the ADNPSO algorithm that optimizes networks according both these objectives. Moreover, FAM networks are evolved to maintain (1) genotype diversity of solutions around local optima in the optimization search space, and (2) phenotype diversity in the objective space. Using local Pareto optimality, networks are then stored in an archive to create a pool of base classifiers among which cost-effective ensembles are selected on the basis of accuracy, and both genotype and phenotype diversity. Performance of the ADNPSO strategy is compared against AMCSs where learning of FAM networks is guided through mono-and multi-objective optimization, and assessed under different incremental learning scenarios, where new data is extracted from real-world video streams for face recognition. Simulation results indicate that the proposed strategy provides a level of accuracy that is comparable to that of using mono-objective optimization, yet requires only a fraction of its resources.

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Section: Ensembles and Dynamic Moomentioning

confidence: 99%

Evolving Classifier Ensembles using Dynamic Multi-objective Swarm Intelligence

Connolly¹,

Granger²,

Sabourin³

2013

Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods

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“…MOMA [13,14] is the multi-objective optimization version of memetic algorithm (MA) [15]. It has been successfully applied to many fields, such as knapsack problems [16], dynamic location problems [17], art classifiers [18], transmission network expansion planning [19] and environmental power unit commitment [20]. The MOMA used in this paper is a combination of NSGA-II [21] and Simulated Annealing (SA) [22].…”

Section: Introductionmentioning

confidence: 99%

The parameter optimization of kalman filter based on multi-objective memetic algorithm

Huo

Cai

Gong

et al. 2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Generally, there are two objectives in the optimization of the measurement noise covariance matrix R of Kalman filter. However, most of the traditional optimization methods of Kalman filter only focus on one objective. In this paper, we proposed a new method to optimize the parameter R based on Multi-Objective Memetic Algorithm (MOMA). Compared with traditional methods, it can optimize multiple objectives simultaneously. In this method, the decision vector is the diagonal elements of matrix R, the first objective function f1 is the mean of the residual vectors, and the second objective function f2 is the degree of mismatching between the actual value of the residual covariance with its theoretical value. In the MOMA, the global search based on NSGA-II is utilized to minimize the two objective functions, and the local search based on Simulated Annealing (SA) is just used to minimize the f1. The experimental results demonstrate that the Kalman filter optimized by MOMA, namely MOMA-Kalman, can get much smaller filtering error than regular Kalman filter and other adaptive filter algorithms, such as SageHusa-Kalman and Fuzzy-Kalman.

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Multi-objective memetic evolution of ART-based classifiers

Cited by 2 publications

References 15 publications

Evolving Classifier Ensembles using Dynamic Multi-objective Swarm Intelligence

Evolving Classifier Ensembles using Dynamic Multi-objective Swarm Intelligence

The parameter optimization of kalman filter based on multi-objective memetic algorithm

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