Intelligent Optimization via Learnable Evolution Model

Authors:
Ryszard S. Michalski;Janusz Wojtusiak;Kenneth A. Kaufman
Affiliations:
George Mason University;George Mason University;George Mason University
Venue:
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Year:
2006

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Abstract

A new method for optimizing complex functions and systems is described that employs Learnable Evolution Model (LEM), a form of non-Darwinian evolutionary computation guided by machine learning. LEM's main novelties are operators for creating new individuals that include hypothesis generation, which learns rules indicating subareas in the search space likely containing the optimum, and hypothesis instantiation, which populates these subareas with new candidate solutions. LEM3, the newest and most advanced implementation of learnable evolution, is briefly described and experimentally compared with other evolutionary computation programs on selected function optimization problems. We also describe two specialized LEM-based systems for heat exchanger optimization.