Author Type

Graduate Student

Date of Award

2022

Document Type

Dissertation

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Barenholtz, Elan

Thesis/Dissertation Co-Chair

Hahn, William

Abstract

One basic goal of artificial learning systems is the ability to continually learn throughout that system’s lifetime. Transitioning between tasks and re-deploying prior knowledge is thus a desired feature of artificial learning. However, in the deep-learning approaches, the problem of catastrophic forgetting of prior knowledge persists. As a field, we want to solve the catastrophic forgetting problem without requiring exponential computations or time, while demonstrating real-world relevance. This work proposes a novel model which uses an evolutionary algorithm similar to a meta-learning objective, that is fitted with a resource constraint metrics. Four reinforcement learning environments are considered with the shared concept of depth although the collection of environments is multi-modal. This system shows preservation of some knowledge in sequential task learning and protection of catastrophic forgetting in deep neural networks.

Extent

108 p.

Comments

Includes bibliography.|Dissertation (Ph.D.)--Florida Atlantic University, 2022.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.

Identifier

FA00013896

Format

pdf

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