From Optimization to Learning in Changing Environments: The Pittsburgh Immune Classifier System

A simple computational model of secondary immune response is used to provide a Pittsburgh style classiier system with the ability to improve its reaction to already encountered situations in a dynamical cyclic learning environment. Main results obtained with our core algorithm (YaSais) on Time Dependent Optimization problems are brieey reminded before to introduce the Pitts-burgh Immune Classiier System (PICS) which is then experimentally evaluated on both a static and dynamical multiplexer problem. Eventually, the Lazy Optimality EEect, keystone of YaSais' eeciency, is re-examinated in PICS. Suggested enhancements are then experimentally evaluated.

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