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We propose a genetic algorithm (GA) based equalization approach for direct sequence ultra-wideband (DS-UWB) wireless communications, where GA is combined with a RAKE receiver to combat the inter-symbol interference (ISI) due to the frequency selective nature of UWB channels for high data rate transmission. Simulation results show that the proposed GA based structure significantly outperforms the RAKE...
We investigate the effects of two different scaling methods and two mutation types on the performance of a genetic algorithm (GA) based equalization approach for direct sequence Ultra-wideband (DS-UWB) wireless communications. Also, we use adaptive generation along with some other parameters to know their effects on the performance in comparison to a previous work, where GA is combined with a RAKE...
The paper addresses the sunspot prediction problem utilising a novel strategy for evaluating individualpsilas relative strengths and weaknesses, by representing these in the form of a binary string fitness characterisation (BSFC), in addition to an overall fitness value for each individual. Utilising a combination of the BSFC and a pair-wise mating strategy, comparative partner selection (CPS), appears...
The genetic algorithm (GA) is a well studied technique, which can obtain efficiently a near-optimal solution with much lower computational complexity compared to the maximum likelihood detection (MLD) approach. In overloaded MIMO systems, since the number of near equal-power co-channel interferers is higher than the number of receive antennas, the interference cannot be completely removed by spatial...
The premise behind all evolutionary methods is ldquosurvival of the fittest,rdquo and consequently, individuals require a quantitative fitness measure. This paper proposes a novel strategy for evaluating individual's relative strengths and weaknesses, as well as representing these in the form of a binary string fitness characterization (BSFC); in addition, as customary, an overall fitness value is...
Externally detected vibroarthrographic (VAG) signals contain information that can be used to characterize certain pathological aspects of the knee joint. To classify VAG signals as normal or abnormal, we propose to apply both the linear and nonlinear strict 2-surface proximal (S2SP) classifiers based on statistical parameters derived from VAG signals and selected by using a genetic algorithm (GA)...
A dataset of 57 breast mass mammographic images, each with 22 features computed, was used in this investigation. The extracted features relate to edge-sharpness, shape, and texture. The novelty of this paper is the adaptation and application of genetic programming (GP). To refine the pool of features available to the GP classifier, we used five feature-selection methods, including three statistical...
This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP) based on Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. Fisher criterion is employed to help GP optimize features whose values corresponding to pattern vector belonging to the same class are extremely similar while those corresponding...
The feature extraction is one of the major challenges for the pattern recognition. This helps to maximise the useful information from the raw data in order to make the classification effective and simple. In this paper, one of the machine learning approaches, genetic programming (GP), is employed to extract features from the raw vibration data taken from a rotating machine with several different conditions...
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