An improved GA and a novel PSO-GA-based hybrid algorithm

Shi, Xiaohu; Liang, Yanchun; Lee, H. P.; Lu, C.; Wang, L. M.

doi:10.1016/j.ipl.2004.11.003

Cited by 311 publications

(129 citation statements)

References 14 publications

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“…A hybrid evolutionary algorithm -PSO method is proposed by Shi et al [59]. The hybrid approach executes the two systems simultaneously and selects P individuals from each system for exchanging after the designated N iterations.…”

Section: Evolutionary Algorithms Assisted By Particle Swarm Optimizationmentioning

confidence: 99%

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

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Summary. Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.

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Section: Evolutionary Algorithms Assisted By Particle Swarm Optimizationmentioning

confidence: 99%

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Groşan

Abraham

2007

Studies in Computational Intelligence

150

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“…The CPSO algorithm performs well in the early iterations (i.e., quickly converging towards an optimum in the first period of iterations), while has problems reaching a near optimal solution in some function optimization problems. Various attempts have been made to improve the performance of CPSO, which can be found in literatures [26][27][28][29][30][31][32][33].…”

Section: Canonical Pso Modelmentioning

confidence: 99%

Optimization based on symbiotic multi-species coevolution

Chen

Zhu

2008

Applied Mathematics and Computation

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“…Between the most promising EAs for this task, the main advantage of PSO over GAs is that the algorithm provides more profound intelligent background, and it can be performed more easily than GAs [17]. Also, the computation time in PSO is usually less than in GAs, because all the particles in PSO tend to converge to the best solution rather quickly [16].…”

Section: Introductionmentioning

confidence: 99%

“…Linear feature transformation methods, such as linear discriminant analysis (LDA) [10], principal component analysis (PCA) [11], and independent component analysis (ICA) [12], can be considered as the earliest forms of feature synthesis, as the features are indeed operated and changed during these transformations. To date, several feature synthesis approaches have been proposed [13], which have shown certain improvements over many types of classification problems.Between the most promising EAs for this task, the main advantage of PSO over GAs is that the algorithm provides more profound intelligent background, and it can be performed more easily than GAs [17]. Also, the computation time in PSO is usually less than in GAs, because all the particles in PSO tend to converge to the best solution rather quickly [16].…”

mentioning

confidence: 99%

Multi-dimensional evolutionary feature synthesis for content-based image retrieval

Kıranyaz

Pulkkinen

İnce

et al. 2011

2011 18th IEEE International Conference on Image Processing

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Abstract-Although there is a wide variety of low-level audio features for content-based audio indexing and retrieval, they m ay lack the discrimination power needed for accurate description of the aural content, leading into a poor content-based retrieval performance. Furthermore, manual selection of features among a vast collection may easily lead into sub-optimal solutions. In this paper, we propose an evolutionary feature synthesis technique, which co-exists with a feature selection scheme. The synthesis process seeks for the optimal linear / nonlinear operators and feature weights from a pre-defined search space, so as to synthesize a highly discriminative set of new (artificial) features from the set of selected features. The evolutionary search process in the multi-dimensional solution space is based on multi-dimensional particle swarm optimization (MD PSO) algorithm, along with a fractional global best formation (FGBF) technique. Unlike in many existing feature generation approaches found in the literature, the dimension of the synthesized feature vector is also optimized during the process. The synthesized features by the proposed approach are compared with original audio descriptors in an extensive set of retrieval tasks. The experimental results clearly demonstrate a crucial improvement of up to 15-25% in the retrieval performance. Moreover, the proposed synthesis technique surpasses the performance of the artificial neural networks for retrieving accurate audio content.Index Terms-Content based retrieval, Evolutionary computation, Feature extraction, Particle swarm optimization. I. INTRODUCTIONThe features can be numerical or nominal scalars or vectors describing some specific characteristics of the data, such as, in case of audio signals, tonality or fundamental frequency. The accuracy of the content-based audio retrieval is strongly dependent on the description quality and discrimination capability of the features. Unfortunately, despite the enormous number of different feature extraction methods available in the literature, most of them have significant limitations and drawbacks in describing the audio content, so that the current audio content search techniques cannot really cope up with the human perceptual auditory system. This lack of semantic representation, also known as the "semantic gap", has led researchers to search and acquire several promising ideas for improving the discrimination power of the low-level (primitive) features. Another reason is the so-called curse of dimensionality phenomenon [2], which basically states that in high dimensions the data becomes too sparse for any decent statistical or structural analysis. In the feature selection scheme, the feature vector dimension is lowered by selectively choosing an expressive and compact set of features among a possibly much larger original set. Evolutionary algorithms, such as genetic algorithms (GA) [3] and genetic programming (GP) [4], are 978-1-4673-2821-0/13/$31.00 ©2013 IEEE popular among many other feature selection approaches...

show abstract

An improved GA and a novel PSO-GA-based hybrid algorithm

Cited by 311 publications

References 14 publications

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews

Optimization based on symbiotic multi-species coevolution

Multi-dimensional evolutionary feature synthesis for content-based image retrieval

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