Unconstrained and Constrained Blackbox Optimization: the Search Perspective

The SEARCH (Search Envisioned As Relation & Class Hierarchizing) framework developed elsewhere (Kargupta, 1995a; Kargupta & Goldberg, 1995) ooered an alternate perspective toward blackbox optimization (BBO)|optimization in presence of little domain knowledge. The SEARCH framework investigates the conditions essential for transcending the limits of random enumerative search using a framework developed in terms of relations, classes and partial ordering. This paper rst reviews some of the main results of that work. A closed form bound on the sample complexity in terms of the cardinality of the relation space, class space, desired quality of the solution and the reliability is presented. The class of order-k delineable problems that can be eeciently solved is identiied and some general algorithm independent strategies for making BBO algorithms eecient are identiied. This paper also introduces an extension of SEARCH for constrained BBO (cBBO)|the cSEARCH framework. Fundamental issues of cBBO are viewed in the light of SEARCH and analyzed. Finally, the SEARCH perspective of genetic algorithms is developed to identify their strengths and weaknesses.