Analysis of Approximation Ability of Evolutionary Optimization

Modified: 2016/02/16 13:30 by admin - Uncategorized
Evolutionary algorithms are most commonly used to obtain "good-enough" solutions in practice, which relates to their approximation ability.


  • Analysis framework. In the AIJ'12 (PDF) paper, we proposed a framework to characterize the approximation ability of a kind of evolutionary algorithms, leading to a general approximation guarantee. On k-set cover problem, it can achieve the currently best-achievable approximation ratio, revealing the advantage of evolutionary algorithms over the well known greedy algorithm.

  • Crossover is helpful: In the AIJ'13 (PDF), we disclosed that crossover operator can help fulfill the Pareto front in multi-objective optimization tasks. As a consequence, on the bi-objective Minimum Spanning Tree problem, a multi-objective EA with a crossover operator is proved to improve the running time from that without the crossover for achieving a 2-approximate solution.

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