Adaptive learning of process control and profit optimization using a classifier system

Authors:
A. H. Gilbert;Frances Bell;Christine L. Valenzuela
Affiliations:
School of Computing and Maths University of Teesside Middlesbrough Cleveland, TS1 3BA United Kingdom;School of Computing and Maths University of Teesside Middlesbrough Cleveland, TS1 3BA United Kingdom;School of Computing and Maths University of Teesside Middlesbrough Cleveland, TS1 3BA United Kingdom
Venue:
Evolutionary Computation
Year:
1995

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Abstract

A classifier system is used to learn control and profit optimization of a batch chemical reaction. Ability to learn different market conditions and changes to reaction parameters is demonstrated. The profit sharing algorithm is used for apportionment of credit. The greater effectiveness of the use of the genetic algorithm over apportionment of credit alone or the random replacement of low strength rules is also shown. The classifier system is unusual in having more than one action per rule.