This talk explores Genetic Algorithms (GAs), a type of Evolutionary Algorithm (EA), and demonstrates their application in optimizing the timeline and budget of software engineering projects.
Leveraging my master's research on evolutionary algorithms, I will share insights into their principles and methodologies.
The simulation of Darwinian evolution in GAs offers an interesting approach to problem-solving, which I believe will captivate and benefit the audience.

The talk will begin with an introduction to Evolutionary Algorithms (EAs), covering their definition, a brief historical context, and an overview of different types, before focusing specifically on Genetic Algorithms (GAs).
I will then explain how GAs work, covering key concepts such as population generation, fitness evaluation, selection, and genetic operators.
To make these concepts easily digestible for the audience, I will provide a high-level overview and use visual aids designed to be suitable for beginners as well as seasoned professionals.

Next, I will explore some use cases of GAs in solving optimization problems and narrow down to my software project example.
Using this software project planning example, I will highlight the project lifecycle stages from planning to deployment, emphasizing how GAs can tailor project timelines to meet client requirements within budget constraints.
Following this, I will delve into the algorithm used for optimizing software project timelines and conduct a live demonstration to showcase its practical application.

In conclusion, I will underscore the significance of GAs and encourage the audience to consider how they might apply these algorithms to optimize problems in their own work.