Enhancing Evolutionary Algorithm Performance with Knowledge Transfer and Asynchronous Parallelism
| dc.contributor.advisor | Luke, Sean | |
| dc.contributor.advisor | De Jong, Kenneth A | |
| dc.creator | Scott, Eric O | |
| dc.date.accessioned | 2023-03-17T19:05:50Z | |
| dc.date.available | 2023-03-17T19:05:50Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Search and optimization problems are widespread throughout business and engineering, but these computational problems are often complex enough that they must be approached with heuristic algorithms. Evolutionary algorithms (EAs) offer a very general framework for solving many of these tasks, but their computational complexity can be difficult to manage when they are applied to mature and large-scale classes of problems. From my experience working on EA applications, I became concerned about two EA efficiency challenges that have been inadequately addressed to date: 1) parallelization strategies for evolutionary algorithms routinely suffer from idle CPU resources that go unused, and 2) customizing evolutionary algorithms with the domain-specific prior knowledge that they require in order to solve useful problems often requires costly and time-consuming research programs. In response to the idle-resources problem, I have engaged in a detailed study of asynchronous steady-state evolutionary algorithms (ASEAs) and their ability to better utilize large clusters of CPU resources. I studied issues of speedup, evaluation-time bias, and excess computational effort in ASEAs—concluding in particular that evaluation-time bias is a less severe problem for these algorithms than many practitioners have assumed. Next, I have addressed the problem of prior knowledge in EAs by engaging in a broad preliminary study of evolutionary knowledge transfer (EKT) and multi-task optimization. Motivated by natural examples of "innovation engines'' that repurpose solutions to past tasks to find solutions to complex future tasks, I studied issues of transferability in different problem classes, negative transfer, and representation-based knowledge transfer approaches for evolutionary algorithms. My contributions in this area include proofs of a new set of no-free-lunch theorems for various types of transfer optimization, and several novel algorithms to address EKT challenges—including a many-source population-seeding algorithm that avoids negative transfer fairly easily, a multi-task Cartesian genetic programming approach, and a representation-learning algorithm that is able to learn and transfer genotype-phenotype maps across problem classes. | |
| dc.format.extent | 317 pages | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier.uri | https://hdl.handle.net/1920/13201 | |
| dc.language.iso | en | |
| dc.rights | Copyright 2022 Eric O Scott | |
| dc.rights.uri | https://rightsstatements.org/vocab/InC/1.0 | |
| dc.subject | Evolutionary algorithms | |
| dc.subject | Evolutionary Computation | |
| dc.subject | Multi-Task Learning | |
| dc.subject | Optimization | |
| dc.subject | Transfer Learning | |
| dc.subject.keywords | Artificial intelligence | |
| dc.title | Enhancing Evolutionary Algorithm Performance with Knowledge Transfer and Asynchronous Parallelism | |
| dc.type | Text | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | George Mason University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Ph.D. in Computer Science |
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