Heritage factors: Extending guided evolutionary simulated annealing

In GESA, a set of independent SA chains are maintained, with no recombination and no sharing of solutions. In each chain, each current state, called the parent state, generates a number of child states using a domain-dependent neighborhood operator, The most fit child is deterministically determined, and then is allowed to replace the parent with a logistic probability. The number of child states that each parent is allowed to generate in each iteration is dependent on the quality of the solutions produced by this chain in the past iteration. We propose a variation, based on what we call heritage factors, that makes the number of offspring a parent is allowed to generate dependent on the previous history of his "genetic line" as well as on the results of the immediate previous iteration. We compare the performance of our variation against that of the original GESA on a 50-city 12-concentrator instance of the concentrator assignment problem.