NIMS Lab Evolutionary Computation Code Distributions
The following code distributions are made available by graduates of the NIMS
Lab for research purposes alone. They are also most definately developed under a *research
coding* context and provided AS IS!
- Hierarchical code reuse and task
transfer: this is half of Stephen Kelly’s PhD
thesis in which
he demonstrated the ability to evolve policies for playing different tasks (e.g. possession and
shooting on goal) and then learn how to redeploy these as a strategy for playing Half field offence.
Issues of importance include the maintenance of diversity without recourse to task specific metrics.
The framework is also deployed under Ms PacMan and the resulting policy demonstrated to reach the
same quality as state-of-the-art results from Monte Carlo Tree Search and neuro-evolution. Code
distribution comes with a couple of animations for illustrating the resulting policies in action.
- Symbolic bid-based GP : Phython distribution originally developed
by Jessica Pauli de Castro Bonson. Two versions available depending on whether you want
classification or temporal sequence learning (most of the code is common). Both of these make
use of dual diversity mechanisms (team diversity and normalized compression distance) in
addition to the task specific fitness function (see Kelly and Heywood
- Symbiotic bid-based GP : C++
distribution as originally developed by Peter Lichodzijewski. Being the 'original' SBB
base this assumes implicit fitness sharing as the diversity mechanism (no radii parameter
- Symbiotic bid-based GP : Java
distribution (SSBJ) developed by Robert Smith. Also assumes implicit fitness sharing as
- Symbiotic Evolutionary Subspace
Clustering : originally developed by Ali Vahdat.