parameter-less local optimizer with linkage identification for deterministic order-k decomposable problems
A simple parameter-less local optimizer able to solve deterministic problems with building blocks of bounded order is proposed in this article. The algorithm is able to learn and use linkage information during the run. The algorithm is algorithmically simple, easy to implement and with the exception of termination condition, it is completely parameter-free---there is thus no need to tune the population size and other parameters to the problem at hand. An empirical comparison on 3 decomposable functions, each with uniformly scaled building blocks of size 5 and 8, was carried out. The algorithm exhibits quadratic scaling with the problem dimensionality, but the comparison with the extended compact genetic algorithm and Bayesian optimization algorithm shows that it needs lower or comparable number of fitness function evaluations on the majority of functions for the tested problem dimensionalities. The results also suggest that the efficiency of the local optimizer compared to both the estimation-of-distribution algorithms should be better for problem sizes under at least a few hundreds of bits.
local optimization estimation-of-distribution algorithms linkage learning evolutionary algorithms