Sushil J. Louis
Genetic Adaptive Systems LAB
Dept. of Computer Science
University of Nevada
Reno, NV 89557
sushil@cs.unr.edu
Yongmian Zhang
Genetic Adaptive Systems LAB
Dept. of Computer Science
University of Nevada
Reno, NV 89557
We present and use a sequence similarity metric to solve sets of similar problems with case injected genetic algorithms. Rather than starting anew on each problem, we periodically inject a genetic algorithm's population with appropriate intermediate solutions to similar, previously solved problems. Using simple syntactic similarity measures, our experimental results from optimizing a series of traveling salesman problems demonstrates the robustness of our approach. Results show that compared to a randomly initialized genetic algorithm, our system learns to take decreasing time to provide better solutions to a new problem as it gains experience from solving other similar problems.