NOTE: Moving this meetup back one week to Nov. 1st!
We have a deadly dual presentation on Genetic Algorithms! Jermey Fisher, Director of DST's Advanced Eng. & Data Science group, and Robert McAnany, machine learning enthusiast and retired engineer, promise not to disappoint!
If you have a large data set, there are many machine learning techniques that can help find interesting patterns quickly. Some problems, however, don't come with data sets. For example, minimizing drive time for a delivery vehicle. This is a combinatorial optimization problem. They pop up everywhere. The only known optimal solution is an exhaustive search. For even a modest size problem running on the fastest computer, that can take years. If you don't want to wait that long, a genetic algorithm can help. Wikipedia defines genetic algorithms this way: "In the field of mathematical optimization, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (...) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms (...) employ techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover."
https://en.wikipedia.org/wiki/Genetic_algorithm
If that definition leaves you with more questions than answers, come join us to really understand what a genetic algorithm is and how it works. See some examples and learn to recognize problems where they apply.


Robert McAnany is a mechanical engineer specialized in the design of automation equipment. He took early retirement from Hallmark recently to focus on machine learning.
Jeremy Fisher is Director of Advanced Engineering and Data Science for DST's Applied Analytics Group. His responsibilities include ensuring that DST has the skills, processes, and the technology to deliver on its vision for commercializing Big Data and deep analytics.