Adaptive group mutation for tackling deception in genetic search

Copyright @ 2004 WSEASIn order to study the efficacy of genetic algorithms (GAs), a number of fitness landscapes have been designed and used as test functions. Among these functions a family of deceptive functions have been developed as difficult test functions for comparing different implementations of GAs. In this paper an adaptive group mutation (AGM), which can be combined with traditional bit mutation in GAs, is proposed to tackle the deception problem in genetic searching. Within the AGM, those genes that have converged to certain threshold degree are adaptively grouped together and subject to mutation together with a given probability. To test the performance of the AGM, experiments were carried out to compare GAs that combine the AGM and GAs that use only traditional bit mutation with a number of suggested “standard” fixed mutation rates over a set of deceptive functions as well as non-deceptive functions. The results demonstrate that GAs with the AGM perform better than GAs with only traditional bit mutation over deceptive functions and as well as GAs with only traditional bit mutation over non-deceptive functions. The results show that the AGM is a good choice for GAs since most problems may involve some degree of deception and deceptive functions are difficult for GAs

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered

Developing redundant binary representations for genetic search

This paper considers the development of redundant representations for evolutionary computation. Two new families of redundant binary representations are proposed in the context of a simple mutationselection evolutionary model. The first is a family of linear encodings in which the connectivity of the search space may be designed directly via a decoding matrix. The second is a family of representations exhibiting various degrees of neutrality, and is constructed using mathematical tools from error-control coding theory. The study of these representations provides additional insight into the properties of redundant encodings, such as synonymity, locality, and connectivity, and into their interrelationships

Developing and Testing Digital Twins for Vehicle Collision Prediction: A Machine Learning and Genetic Search Algorithm Approach

This thesis focuses on developing a digital twin which can predict and avoid collisions. The digital twin does this by using different machine learning models that are trained on data from the SVL Simulator. By harnessing the power of machine learning, the digital twin demonstrates promising abilities in collision prediction and prevention. Additionally, a genetic search algorithm is developed to generate specialized testing data, enabling comprehensive evaluation of the digital twin's performance. The central contribution of this research lies in exploring the viability of utilizing test data that is generated by a genetic search algorithm to evaluate the performance of the digital twin. By employing the genetic search algorithm to generate data resembling real collision scenarios, classified as collisions, an interesting evaluation framework is established. Through the evaluation process, which involves analyzing the number of accurately classified collisions by the digital twin, insights are gained into the model's effectiveness in predicting collisions. This contributes to the ongoing efforts in enhancing the accuracy of collision prediction systems, ultimately leading to improved safety measures in autonomous driving and intelligent transportation systems

Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R

Matching is an R package which provides functions for multivariate and propensity score matching and for finding optimal covariate balance based on a genetic search algorithm. A variety of univariate and multivariate metrics to determine if balance actually has been obtained are provided. The underlying matching algorithm is written in C++, makes extensive use of system BLAS and scales efficiently with dataset size. The genetic algorithm which finds optimal balance is parallelized and can make use of multiple CPUs or a cluster of computers. A large number of options are provided which control exactly how the matching is conducted and how balance is evaluated.