We have introduced two crossover operators, MMX-BLXexploit and
MMX-BLXexplore, for simultaneously solving multiple feature/subset selection
problems where the features may have numeric attributes and the subset sizes
are not predefined. These operators differ on the level of exploration and
exploitation they perform; one is designed to produce convergence controlled
mutation and the other exhibits a quasi-constant mutation rate...
the characteristic of these operators by evolving pattern detectors to
distinguish alcoholics from controls using their visually evoked response
potentials (VERPs). This task encapsulates two groups of subset selection
problems; choosing a subset of EEG leads along with the lead-weights (features
with attributes) and the other that defines the temporal pattern that
characterizes the alcoholic VERPs. We observed better generalization
performance from MMX-BLXexplore. Perhaps, MMX-BLXexploit was handicapped by not
having a restart mechanism. These operators are novel and appears to hold
promise for solving simultaneous feature selection problems.