Reusing Building Blocks of Extracted Knowledge to Solve Complex, Large-Scale Boolean Problems
Abstract
Evolutionary computation techniques have had limited capabilities in solving large-scale problems due to the large search space demanding large memory and much longer training times. In the work presented here, a genetic programming like rich encoding scheme has been constructed to identify building blocks of knowledge in a learning classifier system. The fitter building blocks from the learning system trained against smaller problems have been utilized in a higher complexity problem in the domain to achieve scalable learning. The proposed system has been examined and evaluated on four different Boolean problem domains: 1) multiplexer, 2) majority-on, 3) carry, and 4) even-parity problems. The major contribution of this paper is to successfully extract useful building blocks from smaller problems and reuse them to learn more complex large-scale problems in the domain, e.g., 135-bit multiplexer problem, where the number of possible instances is
- Publication:
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IEEE Transactions on Evolutionary Computation
- Pub Date:
- 2014
- DOI:
- Bibcode:
- 2014ITEC...18..465I
- Keywords:
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- Building blocks;
- code fragments;
- genetic programming;
- layered learning;
- learning classifier systems;
- scalability