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Abstract-Genetic algorithms are powerful tools for knearest neighbors classifier optimization. While traditional knn classification techniques typically employ Euclidian distance to assess pattern similarity, other measures may also be utilized. Previous research demonstrates that GAs can improve predictive accuracy by searching for optimal feature weights and offsets for a cosine similarity-based knn classifier. GA-selected weights determine the classification relevance of each feature, while offsets provide alternative points of reference when assessing angular similarity. Such optimized classifiers perform competitively with other contemporary classification techniques.This paper explores the effectiveness of GA weight and offset optimization for knowledge discovery using knn classifiers with varying similarity measures. Using Euclidian distance, cosine similarity, and Pearson correlation, untrained classifiers are compared with weightoptimized classifiers for several datasets. Simultaneous weight and offset optimization experiments are also performed for cosine similarity and Pearson correlation. This type of optimization represents a novel technique for maximizing Pearson correlation-based knn performance. While unoptimized cosine and Pearson classifiers often perform worse than their Euclidian counterparts, optimized cosine and Pearson classifiers typically show equivalent or improved performance over optimized Euclidian classifiers. In some cases, offset optimization provides further improvement for knn classifiers employing cosine similarity or Pearson correlation.

Lossy image compression algorithms sacrifice perfect image reconstruction in favor of decreased storage requirements. Previous research demonstrates that a genetic algorithm can improve image reconstruction in the presence of quantization error by replacing the wavelet reconstruction coefficients with a set of evolved coefficients. This paper expands previous research efforts by using an improved fitness function, exploring standard versus local genetic search operators, and evolving coefficient sets that perform quite well for multi-resolution analysis (MRA). Test results indicate that our improved evolutionary system consistently outperforms the standard discrete wavelet transform (DWT) for image reconstruction under compression conditions which are subject to quantization error.

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This paper describes the automatic discovery, via an Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), of vectors of real-valued coefficients representing matched forward and inverse transforms that outperform the 9/7 Cohen-Daubechies-Feauveau (CDF) discrete wavelet transform (DWT) for satellite image compression and reconstruction under conditions subject to quantization error. The best transform evolved during this study reduces the mean squared error (MSE) present in reconstructed satellite images by an average of 33.78% (1.79 dB), while maintaining the average information entropy (IE) of compressed images at 99.57% in comparison to the wavelet. In addition, this evolved transform achieves 49.88% (3.00 dB) average MSE reduction when tested on 80 images from the FBI fingerprint test set, and 42.35% (2.39 dB) average MSE reduction when tested on a set of 18 digital photographs, while achieving average IE of 104.36% and 100.08%, respectively. These results indicate that our evolved transform greatly improves the quality of reconstructed images without substantial loss of compression capability over a broad range of image classes.