I am a third-year student at SNC majoring in computer science. I work as a software developer for robotics and autonomy at Infinity Machines. I have interests in all things computers. One of those interests is machine learning.
I am a second-year student at St. Norbert College, majoring in Computer Science with minors in Data Analytics and Mathematics. Outside of academics, I enjoy staying active and like fashion and soccer. I am currently working as a Research Analyst Fellow with the Center for Business, Economics, and Analytics at SNC.
Contact: LinkedIn
Genetic algorithms are a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution.
Genetic algorithms were first introduced by John Holland in the 1960s, marking a significant advancement in the field of evolutionary computation and artificial intelligence. Over the years, these algorithms have evolved and found widespread applications in diverse domains, ranging from engineering and finance to bioinformatics and robotics. The term genetic algorithm originated from the pioneering work of John Holland, who developed these algorithms while exploring computational models of adaptation and evolution. Initially, genetic algorithms were formulated to simulate processes of natural selection and genetics, laying the foundation for their application in solving complex optimization problems. Since their inception, genetic algorithms have undergone continuous refinement and adaptation, leading to the development of more sophisticated variants and hybrid approaches. The evolution of genetic algorithms has been marked by advancements in genetic operations, selection strategies, and their integration with other AI techniques, resulting in their widespread adoption across various industries. The ST5 X-band antenna was designed thanks to a genetic algorithm. This type of antenna is best for a certain radiation pattern and is much more efficient than standard antennas (for instance, helical antenna), partly because of its asymmetrical shape.
