Algorithms for combinatorial optimization in real-time and their automated refinements by genetics-based learning

The goal of this research is to develop a systematic, integrated method of designing efficient search algorithms that solve optimization problems in real time. Search algorithms studied in this thesis comprise meta-control and primitive search. The class of optimization problems addressed are called combinatorial optimization problems, examples of which include many NP-hard scheduling and planning problems, and problems in operations research and artificial-intelligence applications. The problems we have addressed have a well-defined problem objective and a finite set of well-defined problem constraints. In this research, we use state-space trees as problem representations. The approach we have undertaken in designing efficient search algorithms is an engineering approach and consists of two phases: (a) designing generic search algorithms, and (b) improving by genetics-based machine learning methods parametric heuristics used in the search algorithms designed. Our approach is a systematic method that integrates domain knowledge, search techniques, and automated learning techniques for designing better search algorithms. Knowledge captured in designing one search algorithm can be carried over for designing new ones.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical Engineering

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Chu, Lon-Chan

Contributors dc:contributor

• Wah, Benjamin W.

• Engineering, Electronics and Electrical
• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1994 Chu, Lon-Chan

Language dc:language

eng

Identifier

AAI9512334
(UMI)AAI9512334

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/20149