A meta-learning system based on genetic algorithms

Eric Pellerin; Luc Pigeon; Sylvain Delisle

doi:10.1117/12.542205

12 April 2004 A meta-learning system based on genetic algorithms

Eric Pellerin, Luc Pigeon, Sylvain Delisle

Proceedings Volume 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI; (2004) https://doi.org/10.1117/12.542205
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

The design of an efficient machine learning process through self-adaptation is a great challenge. The goal of meta-learning is to build a self-adaptive learning system that is constantly adapting to its specific (and dynamic) environment. To that end, the meta-learning mechanism must improve its bias dynamically by updating the current learning strategy in accordance with its available experiences or meta-knowledge. We suggest using genetic algorithms as the basis of an adaptive system. In this work, we propose a meta-learning system based on a combination of the a priori and a posteriori concepts. A priori refers to input information and knowledge available at the beginning in order to built and evolve one or more sets of parameters by exploiting the context of the system’s information. The self-learning component is based on genetic algorithms and neural Darwinism. A posteriori refers to the implicit knowledge discovered by estimation of the future states of parameters and is also applied to the finding of optimal parameters values. The in-progress research presented here suggests a framework for the discovery of knowledge that can support human experts in their intelligence information assessment tasks. The conclusion presents avenues for further research in genetic algorithms and their capability to learn to learn.

Citation Download Citation

Eric Pellerin, Luc Pigeon, and Sylvain Delisle "A meta-learning system based on genetic algorithms", Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); https://doi.org/10.1117/12.542205

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