Sciweavers

666 search results - page 3 / 134
» When Similar Problems Don't Have Similar Solutions
Sort
View
CEC
2005
IEEE
13 years 11 months ago
Effects of experience bias when seeding with prior results
Abstract- Seeding the population of an evolutionary algorithm with solutions from previous runs has proved to be useful when learning control strategies for agents operating in a c...
Mitchell A. Potter, R. Paul Wiegand, H. Joseph Blu...
ICCBR
1999
Springer
13 years 10 months ago
When Experience Is Wrong: Examining CBR for Changing Tasks and Environments
Case-based problem-solving systems reason and learn from experiences, building up case libraries of problems and solutions to guide future reasoning. The expected bene ts of this l...
David B. Leake, David C. Wilson
GECCO
2006
Springer
161views Optimization» more  GECCO 2006»
13 years 9 months ago
Instance similarity and the effectiveness of case injection in a genetic algorithm for binary quadratic programming
When an evolutionary algorithm addresses a sequence of instances of the same problem, it can seed its population with solutions that it found for previous instances. This techniqu...
Jason Amunrud, Bryant A. Julstrom
EUSFLAT
2007
119views Fuzzy Logic» more  EUSFLAT 2007»
13 years 7 months ago
Similarity of Fuzzy Preference Structures Based on Metrics
We generalize the solution of a multicriterial optimization problem which has been given in [5]. They have used a comparison of the criterion fuzzy preference relations and the ge...
Dana Hlinená, Pavol Král
FLAIRS
2003
13 years 7 months ago
Dispatching Cases versus Merging Case-Bases: When MCBR Matters
Multi-case-base reasoning (MCBR) extends case-based reasoning to draw on multiple case bases that may address somewhat different tasks. In MCBR, an agent selectively supplements i...
David B. Leake, Raja Sooriamurthi