Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits Sushil J. Louis Genetic Algorithm Systems Lab(gaslab) University of Nevada Reno

Outline  Background  What is the technique? GAs + CBR  How do we evaluate the technique? Example problem from Combinational Logic Design  Is the technique useful? Results  Conclusions

Background  Genetic Algorithm augmentation  Deployed systems are expected to confront and solve many problems over their lifetime  How can we increase genetic algorithm performance with experience? Provide GA with a memory Seed the GA population

Case-Based Reasoning  When confronted by a new problem, adapt similar (already solved) problem’s solution to solve new problem Many problems in design are suited to a case- based representation  CBR = Associative Memory + Adaptation  Indexing (similarity) and adaptation are domain dependent

Case Injected Genetic AlgoRithm  Combine genetic “adaptive” search with case-based memory  Case-base provides memory  Genetic algorithm provides adaptation  Questions: What is a case? How do we do Indexing?

What is a Case?  CIGAR Member of the GA’s population (Chromosome) Fitness Generation that this chromosome was created Other

Indexing  Problem similarity We must have a similarity metric over problems  Solution similarity We use hamming distance for binary encodings, sequence similarity for permutation encodings.

Problem Similarity

Solution Similarity