Genetics-based learning of new heuristics: rational scheduling of experiments and generalization

We present new methods for the automated learning of heuristics in knowledge lean applications and for finding heuristics that can be generalized to unlearned domains. These applications lack domain knowledge for credit assignment; hence, operators for composing new heuristics are generally model free, domain independent, and syntactic in nature. The operators we have used are genetics based; examples of which include mutation and cross over. Learning is based on a generate and test paradigm that maintains a pool of competing heuristics, tests them to a limited extent, creates new ones from those that perform well in the past, and prunes poor ones from the pool. We have studied three important issues in learning better heuristics: anomalies in performance evaluation; rational scheduling of limited computational resources in testing candidate heuristics in single objective as well as multiobjective learning; and finding heuristics that can be generalized to unlearned domains. We show experimental results in learning better heuristics for: process placement for distributed memory multicomputers, node decomposition in a branch and bound search, generation of test patterns in VLSI circuit testing, and VLSI cell placement and routing