Abstract
A recently developed approach to evolutionary computation, called Learnable Evolution Model or LEM, employs machine learning to guide processes of generating new populations. The central new idea of LEM is that it generates new individuals not by mutation and/or recombination, but by processes of hypothesis generation and instantiation. The hypotheses are generated by a machine learning system from examples of high and low performance individuals. When applied to problems of function optimization and parameter estimation for nonlinear filters, LEM significantly outperformed the standard evolutionary computation algorithms used in experiments, sometimes achieving two or more orders of magnitude of evolutionary speed-up (in terms of the number of births). An application of LEM to the problem of optimizing heat exchangers has produced designs equal to or exceeding the best human designs. Further research needs to explore trade-offs and determine best areas for LEM application.
Original language | English (US) |
---|---|
Title of host publication | Proceedings of the IEEE Conference on Evolutionary Computation, ICEC |
Pages | 1064-1071 |
Number of pages | 8 |
Volume | 2 |
State | Published - 2000 |
Event | Proceedings of the 2000 Congress on Evolutionary Computation - California, CA, USA Duration: Jul 16 2000 → Jul 19 2000 |
Other
Other | Proceedings of the 2000 Congress on Evolutionary Computation |
---|---|
City | California, CA, USA |
Period | 7/16/00 → 7/19/00 |
All Science Journal Classification (ASJC) codes
- General Engineering
- General Computer Science
- Computational Theory and Mathematics