IEEE Trans Cybern. 2015 Apr;45(4):610-21. doi: 10.1109/TCYB.2014.2331994. Epub 2014 Jul 8.
Inspired by biological evolution, a plethora of algorithms with evolutionary features have been proposed. These algorithms have strengths in certain aspects, thus yielding better optimization performance in a particular problem. However, in a wide range of problems, none of them are superior to one another. Synergetic combination of these algorithms is one of the potential ways to ameliorate their search ability. Based on this idea, this paper proposes an adaptive memetic computing as the synergy of a genetic algorithm, differential evolution, and estimation of distribution algorithm. The ratio of the number of fitter solutions produced by the algorithms in a generation defines their adaptability features in the next generation. Subsequently, a subset of solutions undergoes local search using the evolutionary gradient search algorithm. This memetic technique is then implemented in two prominent frameworks of multiobjective optimization: the domination- and decomposition-based frameworks. The performance of the adaptive memetic algorithms is validated in a wide range of test problems with different characteristics and difficulties.
受生物进化启发，提出了大量具有进化特征的算法。这些算法在某些方面具有优势，因此在特定问题上具有更好的优化性能。然而，在广泛的问题中，它们都没有彼此优越。这些算法的协同组合是改善它们搜索能力的潜在方法之一。基于这一思想，本文提出了一种自适应的进化计算，作为遗传算法、差分进化和分布估计算法的协同。在下一代中，算法在一代中产生的更适合的解决方案数量的比例定义了它们的适应性特征。随后，使用进化梯度搜索算法对解决方案的子集进行局部搜索。然后，这种遗传技术在基于支配和基于分解的两个多目标优化框架中实现。自适应遗传算法的性能在具有不同特点和难度的广泛测试问题中得到验证。
