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We propose a multi-objective method, inspired by the Age Layered Population Structure algorithm, for avoiding premature convergence in evolutionary algorithms, and demonstrate a three-fold performance improvement over comparable methods. Previous research has shown that partitioning an evolving population into age groups can greatly improve the ability to identify global optima and avoid converging to local optima. Here, we propose that treating age as an explicit optimization criterion candoi:10.1145/1830483.1830584 dblp:conf/gecco/SchmidtL10 fatcat:bqf6mst345fdvjo64sghjdobla