Minimum population search - Lessons from building a heuristic technique with two population members

Population-based heuristics can be effective at optimizing difficult multi-modal problems. However, population size has to be selected correctly to achieve the best results. Searching with a smaller population increases the chances of convergence and the efficient use of function evaluations, but it also induces the risk of premature convergence. Larger populations can reduce this risk but can cause poor efficiency. This paper presents a new method specifically designed to work with very small populations. Computational results show that this new heuristic can achieve the benefits of smaller populations and largely avoid the risk of premature convergence.