Population Diversity as a Selection Factor
James Byron, Wayne Iba · 2016
Evolutionary algorithms search for problem solutions by selecting individuals for survival and reproduction with a bias towards higher fitness. Such biases may lead to premature convergence on sub-optimal solutions. A bias toward greater diversity can help delay convergence and broaden the area searched for optimal candidate solutions. We introduce two ways to measure a population's diversity and evaluate how they interact with traditional fitness during selection. We then introduce a mechanism that includes a bias toward greater diversity in addition to traditional accuracy. Using the King-Rook-King chess endgame problem, we demonstrate that including diversity as a selection factor leads to better overall solutions.