CIGAR makes the assumption that similar problems have similar solutions. According to the schema theorem, genetic algorithms process syntactic string similarities and we have shown that storing and injecting syntactically similar solutions leads to increased performance with experience. CIGAR thus combines the strengths of GAs and CBR and avoids the difficult problem of measuring similarities among problems.
Our sample results indicate that CIGAR learns to increase performance
at related tasks as it gains experience. We show that the simple
syntactic similarity measure of hamming distance works well on
combinational circuit design problems. Several general trends can be
picked out. 1) The more similar the problems the larger the
difference in performance between CIGAR and RIGA. 2) The more similar
the problems the more individuals that can be injected without loss of
performance. However, injecting too many individuals often leads to
premature convergence. Since we do not usually know problem similarity
injecting between
of the population size strikes a good
balance. 3) There are several ways to choose individuals to be
injected with little difference in CIGAR performance. We can inject the
closest (individuals in the case-base) to the best individual in the
population, the farthest from the worst, and probabilistic versions of
both. In the spirit of genetic algorithm research we prefer the
probabilistic versions.
We are currently applying CIGAR to problems in object identification, spectroscopic analysis of dense plasmas, and real-time targeting and re-targeting. Because these problems are computationally intensive or require real-time responses, we will be building and investigating a parallel CIGAR implementation, different selection schemes, and different case-base implementations. On the theoretical side we are modeling and quantifying our qualitatively derived parameter values for CIGAR.