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Proceedings Paper

Reducing bathymetric-lidar algorithm uncertainty with genetic programming and the evolutionary multi-objective algorithm design engine
Author(s): Jason Zutty; Domenic Carr; Rodd Talebi; James Rick; Christopher Valenta; Gregory Rohling
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Paper Abstract

In recent years, the field of automated machine learning (autoML) has quickly attracted significant attention both in academia and industry. The driving force is to reduce the amount of human intervention required to process data and create models for classification and prediction, a tedious and arbitrary process for data scientists that may not often result in achieving a global optimum with respect to multiple objectives. Moreover, existing autoML techniques rely on extremely large collections of relatively clean training data, which is not typical of Multi-Domain Battle (MDB) applications. In this paper, we describe a methodology to optimize underwater seafloor detection for airborne bathymetric lidar, an application domain with sparse truth data, leveraging evolutionary algorithms and genetic programming. Our methodology uses the Evolutionary Multi-objective Algorithm Design Engine (EMADE) and a radiometric waveform simulator generating millions of waveforms from which genetic programming techniques select optimal signal processing techniques and their parameters given the goal of reducing Total Propagated Uncertainty (TPU). The EMADE affords several benefits not found in other autoML solutions, including the ability to stack machine learning models, process time-series data using dozens of signal-processing techniques, and efficiently evaluate algorithms on multiple objectives. Given the lack of truth data, we tune EMADE to produce detection algorithms that improve accuracy and reduce relevant measurement uncertainties for a wide variety of operational and environmental scenarios. Preliminary testing indicates successfully reducing TPU and reducing over- and under-prediction errors by 13.8% and 68.2% respectively, foreshadowing using EMADE to assist in future MDB-application algorithm development.

Paper Details

Date Published: 10 May 2019
PDF: 12 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110061U (10 May 2019); doi: 10.1117/12.2519320
Show Author Affiliations
Jason Zutty, Georgia Tech Research Institute (United States)
Domenic Carr, Georgia Tech Research Institute (United States)
Rodd Talebi, Georgia Tech Research Institute (United States)
James Rick, Georgia Tech Research Institute (United States)
Christopher Valenta, Georgia Tech Research Institute (United States)
Gregory Rohling, Georgia Tech Research Institute (United States)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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