Targeted EDA adapted for a routing problem with variable length chromosomes

Targeted EDA (TEDA), an evolutionary algorithm that combines a targeted intervention principle with Estimation of Distribution Algorithms (EDA), is designed to solve optimal control problems where the number of interventions is an element of solution fitness. This paper applies it to a network routing problem and in doing so adapts it to problems involving variable length chromosomes. We show that TEDA can outperform algorithms using standard crossover techniques such as one and two point crossover on this new problem and in doing so we extend the range of problems that TEDA is effective at solving.