A New Genetic Algorithm for Set Covering Problems

An indirect genetic algorithm for the non-unicost set covering problem is presented. The algorithm is a two-stage meta-heuristic, which in the past was successfully applied to similar multiple-choice optimisation problems. The two stages of the algorithm are an ‘indirect’ genetic algorithm and a decoder routine. First, the solutions to the problem are encoded as permutations of the rows to be covered, which are subsequently ordered by the genetic algorithm. Fitness assignment is handled by the decoder, which transforms the permutations into actual solutions to the set covering problem. This is done by exploiting both problem structure and problem specific information. However, flexibility is retained by a self-adjusting element within the decoder, which allows adjustments to both the data and to stages within the search process. Computational results are presented.

Being more certain about random assignment in social policy evaluations

Social experiments have been widely utilised in evaluations of social programmes in the US to identify ‘what works’, whilst in the UK their use is more controversial. This paper explores the paradigmatic, technical and practical issues evaluators confront in using randomised experiments to evaluate social policies. Possible remedies to some of these problems are outlined. It is argued that although no evaluation methodology is problem-free, policy makers and researchers should be more confident about the merits of using random assignment, provided it is used in conjunction with other methodologies more suited to understanding why and how interventions work.