Synthetic Intelligence Forum is excited to convene a presentation about LEAP, which is a library for Evolutionary Algorithms in Python.

Title: LEAP – Library for Evolutionary Algorithms in Python

Abstract: There are generally three types of scientic software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts.

Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software.

Currently there are no Python-based evolutionary computation frameworks that support all three types of scientific software users. Moreover, some support synchronous concurrentness evaluation that do not efficiently use HPC resources.

In this talk, Mark will showcase a new Python-based EC framework that uses an established generalized unified approach to EA concepts to provide an easy to use toolkit for users wishing to use an EA to solve a problem, for researchers to implement novel approaches, and for providing a low-bar to entry to EA concepts for students.

Additionally, this toolkit provides a scalable asynchronousness evaluation implementation friendly to HPC that has been vetted on hardware ranging from laptops to the worlds fastest supercomputer, Summit.

Biography: Mark is a computer scientist with a research focus on evolutionary algorithms (EA) within a High Performance Computing (HPC) context. EAs are well positioned to exploit HPC platforms because of their scalable and embarrassingly parallel nature.

He is a Staff Scientist at the Oak Ridge National Laboratory, which is an ideal organization for researching this area due to ready access to HPC platforms. These include Summit, which is currently one of the most powerful supercomputers in the world.

His research studies Asynchronous Steady-state Evolutionary Algorithms (ASEA) that are typically used for HPC-related work. His most recent work entailed using ASEAs to optimize deep-learner (DL) hyper-parameters and architectures on Summit.

He is interested in understanding the unique dynamics of ASEAs to scale on very large, Summit-sized systems. He is actively engaged in improving understanding of ASEAs on such platforms to provide useful guidelines for practitioners that wish to use ASEAs to solve real-world problems.

Mark earned a PhD in Computer Science at the George Mason University.

Profiles of the host and presenter:
• Vik Pant, PhD – https://www.linkedin.com/in/vikpant
• Mark Coletti, PhD – https://www.linkedin.com/in/macoletti/

Web resources for LEAP:
• Documentation – https://leap-gmu.readthedocs.io/en/latest/
• GitHub – https://github.com/AureumChaos/LEAP
• Research paper – https://www.osti.gov/servlets/purl/1649229

Join Synthetic Intelligence Forum online:
• Website – http://www.synthint.ai
• LinkedIn (Page) – https://www.linkedin.com/company/synthint/
• LinkedIn (Group) – https://www.linkedin.com/groups/12092618/
• YouTube – https://www.youtube.com/c/SyntheticIntelligenceForum

Special Thanks to our Partner:
• ET Business Services