Learning With Case-Injected Genetic Algorithms
Abstract
This paper presents a new approach to acquiring and using problem specific knowledge during a genetic algorithm (GA) search. A GA augmented with a case-based memory of past problem solving attempts learns to obtain better performance over time on sets of similar problems. Rather than starting anew on each problem, we periodically inject a GA's population with appropriate intermediate solutions to similar previously solved problems. Perhaps, counterintuitively, simply injecting solutions to previously solved problems does not produce very good results. We provide a framework for evaluating this GA-based machine-learning system and show experimental results on a set of design and optimization problems. These results demonstrate the performance gains from our approach and indicate that our system learns to take less time to provide quality solutions to a new problem as it gains experience from solving other similar problems in design and optimization.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2004
- Accession Number
- ADA446085
Entities
People
- John Mcdonnell
- Sushil J. Louis
Organizations
- University of Nevada, Reno