更改

==与生物过程的比较==

==与生物过程的比较==

对于演化算法来说，一个可能的局限可能是缺少对基因型和表现型的明确区分。在大自然中，一个受精卵会历经一个成为胚胎发育的过程，然后成为一个成熟的表现型。自然选择以表现型而非基因型为选择标准。这种间接编码被认为可以使得演化过程更健壮（降低致命突变的概率），并提高器官的进化能力<ref>G.S. Hornby and J.B. Pollack. "Creating high-level components with a generative representation for body-brain evolution". ''[[Artificial Life (journal)|Artificial Life]]'', 8(3):223–246, 2002.</ref><ref>Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. [http://www.ofria.com/pubs/2009CluneEtAl.pdf "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding"] {{Webarchive|url=https://web.archive.org/web/20160603205252/http://www.ofria.com/pubs/2009CluneEtAl.pdf |date=2016-06-03 }}. ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.</ref> 。这种间接编码还使得演化能够充分利用环境的规律<ref>J. Clune, C. Ofria, and R. T. Pennock, [http://jeffclune.com/publications/Clune-HyperNEATandRegularity.pdf "How a generative encoding fares as problem-regularity decreases"], in ''PPSN'' (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of ''Lecture Notes in Computer Science'', pp. 358–367, Springer, 2008.</ref>。最近有关[[人工胚胎学]] artificial embryogeny 和[[人工发展系统]] artificial developmental system 的工作也在寻求解决这一局限的方法。基因表达规划这一技术在基因型-表现型系统的探索中取得成功，其中基因型包括固定长度地线性多基因染色体，而表现型则包括了规模可变的多重表达式树或者计算机程序<ref>Ferreira, C., 2001. [http://www.gene-expression-programming.com/webpapers/GEP.pdf "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems"]. ''Complex Systems'', Vol. 13, issue 2: 87–129.</ref>。

对于演化算法来说，一个可能的局限可能是缺少对基因型和表现型的明确区分。在大自然中，一个受精卵会历经一个成为胚胎发育的过程，然后成为一个成熟的表现型。自然选择以表现型而非基因型为选择标准。这种间接编码被认为可以使得演化过程更健壮（降低致命突变的概率），并提高器官的进化能力<ref>G.S. Hornby and J.B. Pollack. "Creating high-level components with a generative representation for body-brain evolution". ''[[Artificial Life (journal)|Artificial Life]]'', 8(3):223–246, 2002.</ref><ref>Jeff Clune, Benjamin Beckmann, Charles Ofria, and Robert Pennock. [http://www.ofria.com/pubs/2009CluneEtAl.pdf "Evolving Coordinated Quadruped Gaits with the HyperNEAT Generative Encoding"] . ''Proceedings of the IEEE Congress on Evolutionary Computing Special Section on Evolutionary Robotics'', 2009. Trondheim, Norway.</ref> 。这种间接编码还使得演化能够充分利用环境的规律<ref>J. Clune, C. Ofria, and R. T. Pennock, [http://jeffclune.com/publications/Clune-HyperNEATandRegularity.pdf "How a generative encoding fares as problem-regularity decreases"], in ''PPSN'' (G. Rudolph, T. Jansen, S. M. Lucas, C. Poloni, and N. Beume, eds.), vol. 5199 of ''Lecture Notes in Computer Science'', pp. 358–367, Springer, 2008.</ref>。最近有关[[人工胚胎学]] artificial embryogeny 和[[人工发展系统]] artificial developmental system 的工作也在寻求解决这一局限的方法。基因表达规划这一技术在基因型-表现型系统的探索中取得成功，其中基因型包括固定长度地线性多基因染色体，而表现型则包括了规模可变的多重表达式树或者计算机程序<ref>Ferreira, C., 2001. [http://www.gene-expression-programming.com/webpapers/GEP.pdf "Gene Expression Programming: A New Adaptive Algorithm for Solving Problems"]. ''Complex Systems'', Vol. 13, issue 2: 87–129.</ref>。

 

==相关技术==

==相关技术==