USING KNOWLEDGE-BASED SYSTEMS WITH HIERARCHICAL ARCHITECTURES TO GUIDE EVOLUTIONARY SEARCH

Abstract

Regional Knowledge is useful in identifying patterns of relationships between variables, and it is particularly important in solving constrained global optimization problems. However, regional knowledge is generally unavailable prior to the optimization search. The questions here are: 1) Is it possible for an evolutionary system to learn regional knowledge during the search instead of having to acquire it beforehand? and 2) How can this regional knowledge be used to expedite evolutionary search? This paper defines regional schemata to provide an explicit mechanism to support the acquisition, storage and manipulation of regional knowledge. In a Cultural Algorithm framework, the belief space "contains" a set of these regional schemata, arranged in a hierarchical architecture, to enable the knowledge-based evolutionary system to learn regional knowledge during the search and apply the learned knowledge to guide the search. This mechanism can be used to guide the optimization search in a direct way, by "pruning" the infeasible regions and "promoting" the promising regions. Engineering problems with nonlinear constraints are tested and the results are discussed. It shows that the proposed mechanism is potential to solve complicated non-linear constrained optimization problems, and some other hard problems, e.g. the optimization problems with "ridges" in landscapes.