Modified Linkage Learning Genetic Algorithm For Difficult Non-stationary Problems

The linkage learning genetic algorithm (LLGA) proposed by Harik (Harik 1997), evolved tight linkage in a bid to solve difficult problems. This paper extends this work to difficult non-stationary problems. The probabilistic expression mechanism of the LLGA is akin to the dominance and polyploidy found in nature. This redundancy of gene expression found in the LLGA was found to benefit the adaptation of the solution to dynamic fitness landscapes. However as the LLGA converges to tight linkage the available diversity decreases considerably. In this study it was found that by allowing disruption of tightly linked structures (with low probability) through the crossover operator and introducing explicit mutation and diploidy, much improvement could be gained in solving non-stationary deceptive problems without relearning the linkage evolved over the run.