An Impact of Cross Over Operator on the Performance of Genetic Algorithm Under Operating System Process Scheduling Problem

Kumar, Rajiv; Gill, Sanjeev; Kaushik, Ashwani

doi:10.1109/csnt.2011.150

Cited by 15 publications

(3 citation statements)

References 3 publications

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“…As the GA continues, the exploitative search is increased until both are performed with the same intensity. Finally, the levels considered for α and β are consistent with other GA implementations [ 53 , 55 ].…”

Section: Optimization Method: Genetic Algorithmsupporting

confidence: 66%

“…Crossover is explorative as it makes a jump to a region somewhere “in between” two (parents) regions [ 52 ]. Because of this, the crossover operator diversifies the population [ 53 ].…”

Section: Optimization Method: Genetic Algorithmmentioning

confidence: 99%

“…Mutation is exploitative as it creates random small deviations, thereby staying near (in the region of) the parent [ 52 ]. While the crossover operator diversifies the population, the mutation operator exploits the new result [ 53 ].…”

Section: Optimization Method: Genetic Algorithmmentioning

confidence: 99%

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Estimation of Phoneme-Specific HMM Topologies for the Automatic Recognition of Dysarthric Speech

Caballero‐Morales

2013

Computational and Mathematical Methods in Medicine

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Dysarthria is a frequently occurring motor speech disorder which can be caused by neurological trauma, cerebral palsy, or degenerative neurological diseases. Because dysarthria affects phonation, articulation, and prosody, spoken communication of dysarthric speakers gets seriously restricted, affecting their quality of life and confidence. Assistive technology has led to the development of speech applications to improve the spoken communication of dysarthric speakers. In this field, this paper presents an approach to improve the accuracy of HMM-based speech recognition systems. Because phonatory dysfunction is a main characteristic of dysarthric speech, the phonemes of a dysarthric speaker are affected at different levels. Thus, the approach consists in finding the most suitable type of HMM topology (Bakis, Ergodic) for each phoneme in the speaker's phonetic repertoire. The topology is further refined with a suitable number of states and Gaussian mixture components for acoustic modelling. This represents a difference when compared with studies where a single topology is assumed for all phonemes. Finding the suitable parameters (topology and mixtures components) is performed with a Genetic Algorithm (GA). Experiments with a well-known dysarthric speech database showed statistically significant improvements of the proposed approach when compared with the single topology approach, even for speakers with severe dysarthria.

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Section: Optimization Method: Genetic Algorithmsupporting

confidence: 66%

“…Crossover is explorative as it makes a jump to a region somewhere “in between” two (parents) regions [ 52 ]. Because of this, the crossover operator diversifies the population [ 53 ].…”

Section: Optimization Method: Genetic Algorithmmentioning

confidence: 99%