2004/03/23 by Shane Legg, Marcus Hutter, Akshat Kumar
Computer Science · #cs.LG #cs.AI
published as Proc. 2004 Congress on Evolutionary Computation (CEC-2004), pages 2144--2151 · 10 pages, 8 figures
arxiv created 2004/03/23 · arxiv updated 2009/12/01
In evolutionary algorithms a critical parameter that must be tuned is that of selection pressure. If it is set too low then the rate of convergence towards the optimum is likely to be slow. Alternatively if the selection pressure is set too high the system is likely to become stuck in a local optimum due to a loss of diversity in the population. The recent Fitness Uniform Selection Scheme (FUSS) is a conceptually simple but somewhat radical approach to addressing this problem - rather than biasing the selection towards higher fitness, FUSS biases selection towards sparsely populated fitness levels. In this paper we compare the relative performance of FUSS with the well known tournament selection scheme on a range of problems.