SGER: Learnable Evolution: Speeding up Evolutionary Computation by Inductive Learning

点击翻译按钮获取中文摘要

IIS-9904078Ryszard Michalski, George Mason University$49,751 - 12 mos SGER: Learnable Evolution - Speeding up Evolutionary Computation byInductive LearningIn this exploratory research the PI will continue work on a new approach to evolutionary computation that he calls Learnable Evolution Model (LEM). In contrast to the Darwinian evolution model that underlies current methods of evolutionary computation, LEM involves an inductive learning process that seeks "reasons" why certain individuals in a population are superior to others in performing a designated class of tasks. These reasons, expressed as inductive hypotheses, are used to create a new generation of individuals at each step of an evolutionary learning process. In an already completed pilot study, LEM has been compared to two standard genetic algorithms in solving different kinds of optimization problems. These problems have been widely used by the evolutionary computation community for testing genetic algorithms. In these experiments, LEM outperformed the genetic algorithms by a wide margin, frequently achieving a speed-up of two or three orders of magnitude. In some cases, LEM found the optimal solution, while genetic algorithms used in the study were still far away from it. Under this award, the PI will conduct theoretical and experimental studies of the LEM approach, testing it on different types of problems, in particular, on very high dimensionality and noisy optimization problems, and system design problems. He will study its limitations, and determine ways of enhancing it by ideas and methods related to his prior work on constructive induction (developed under NSF support). The proposed research may potentially lead to major advances in the fields evolutionary computation and machine learning, which can be applied to a variety of areas including ultra-complex optimization problems, design of engineering systems, knowledge discovery in very large databases, and self-improving intelligent agents.