Search, polynomial complexity, and the fast messy genetic algorithm

Blackbox optimization--optimization in presence of limited knowledge about the objective function--has recently enjoyed a large increase in interest because of the demand from the practitioners. This has triggered a race for new high performance algorithms for solving large, difficult problems. Simulated annealing, genetic algorithms, tabu search are some examples. Unfortuntely, each of these algorithms is creating a separate field in itself and their use in practice is often guided by personal discretion rather than scientific reasons. The primary reason behind this confusing situation is the lack of any comprehensive understanding about blackbox search. This dissertation takes a step toward clearing some of the confusion. The main objectives of this dissertation are: (1) present SEARCH (Search Envisioned As Relation & Class Hierarchizing)--an alternate perspective of blackbox optimization and its quantitative analysis that lays the foundation essential for transcending the limits of random enumerative search; (2) design and testing of the fast messy genetic algorithm.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Kargupta, Hillol

Contributors dc:contributor

• Goldberg, David E.

• Operations Research
• Computer Science

Statement dc:rights

• Copyright 1996 Kargupta, Hillol

Language dc:language

eng

Identifier

AAI9625147
(UMI)AAI9625147

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/19062