Genetic Programming with Memory For Financial Trading

2016 ◽  
pp. 19-34 ◽  
Author(s):  
Alexandros Agapitos ◽  
Anthony Brabazon ◽  
Michael O’Neill
Keyword(s):  
Genetic Programming ◽  
Financial Trading ◽  
With Memory

Memory with Memory in Tree-Based Genetic Programming

2009 ◽  
pp. 25-36 ◽  
Author(s):  
Riccardo Poli ◽  
Nicholas F. McPhee ◽  
Luca Citi ◽  
Ellery Crane
Keyword(s):  
Genetic Programming ◽  
With Memory

scholarly journals Memory with Memory in Genetic Programming

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Riccardo Poli ◽  
Nicholas Freitag McPhee ◽  
Luca Citi ◽  
Ellery Crane
Keyword(s):  
Exact Solution ◽  
Approximate Solution ◽  
Genetic Programming ◽  
Target Problem ◽  
Empirical Tests ◽  
With Memory ◽  
Over Time

We introduce Memory with Memory Genetic Programming (MwM-GP), where we use soft assignments and soft return operations. Instead of having the new value completely overwrite the old value of registers or memory, soft assignments combine such values. Similarly, in soft return operations the value of a function node is a blend between the result of a calculation and previously returned results. In extensive empirical tests, MwM-GP almost always does as well as traditional GP, while significantly outperforming it in several cases. MwM-GP also tends to be far more consistent than traditional GP. The data suggest that MwM-GP works by successively refining an approximate solution to the target problem and that it is much less likely to have truly ineffective code. MwM-GP can continue to improve over time, but it is less likely to get the sort of exact solution that one might find with traditional GP.

Capito-Lunate Fusion with Memory Staples

2015 ◽  
Vol 04 (S 01) ◽  
Author(s):  
M. Solomons
Keyword(s):  
With Memory
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