Multimodal Search with Immune Based Genetic Programming
Multimodal Search with Immune Based Genetic Programming
复制标题
具有基于免疫的遗传编程的多模态搜索
DOI：
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
H. Iba
中科院分区：
文献类型：
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作者：
Yoshihiko Hasegawa;H. Iba
人工免疫系统因其强大的信息处理能力而成为人们极大兴趣的课题。这是因为免疫系统具有一些显着特征，例如记忆能力、针对抗原的单一性、针对动态变化环境的灵活性以及抗体的多样性。迄今为止，受这些免疫特征启发的多种算法已经被提出，并应用于识别、计算机安全、优化等许多问题。本文提出了一种名为多模态搜索遗传规划（MSGP）的优化算法，通过引入免疫学特征来扩展GP，以保持其多样性，从而解决多模态适应度问题。我们通过将该算法应用于人工蚂蚁问题来凭经验证明我们方法的有效性。
Artificial Immune Systems have become the subject of great interest due to their powerful information processing capabilities. This is because the immune system has some salient features such as memorizing ability, singularity against antigens, flexibility against dynamically changing environments, and diversity of antibodies. Up to now, several algorithms inspired by these immune features have been proposed and applied to many problems such as recognition, computer security, optimization, etc. This paper proposes an optimization algorithm named Multimodal Search Genetic Programming (MSGP), which extends GP by introducing immunological features so as to maintain its diversity for the sake of solving the problems with multimodal fitness landscapes. We empirically show the effectiveness of our approach by applying the algorithm to the artificial ant problem.
DOI：
10.1007/bfb0055923
发表时间：
1998
期刊：
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影响因子：
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作者：
Moshe Sipper
通讯作者：
Moshe Sipper