- author = "Moshe Looks",
- title = "Competent Program Evolution",
- school = "Washington University",
- year = "2006",
- type = "Doctor of Science",
- address = "St. Louis, USA",
- month = "11 " # dec,
- keywords = "genetic algorithms, genetic programming",
-
URL = "
http://metacog.org/main.pdf",
-
URL = "
https://openscholarship.wustl.edu/cse_research/216/",
-
DOI = "
doi:10.7936/K7KD1W8C",
- size = "101 pages",
-
abstract = "Heuristic optimization methods are adaptive when they
sample problem solutions based on knowledge of the
search space gathered from past sampling. Recently,
competent evolutionary optimization methods have been
developed that adapt via probabilistic modeling of the
search space. However, their effectiveness requires the
existence of a compact problem decomposition in terms
of prespecified solution parameters.
How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution.
The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability.",
-
notes = "http://metacog.org/doc.html
Technical Report Number: WUCSE-2006-64",
