Research on Genetic Algorithms with Function Division Schemes

具有功能划分方案的遗传算法研究

• 批准号: 10680396
• 负责人: TSUTSUI Shigeyoshi
• 金额: $ 1.86万
• 依托单位: HANNAN UNIVERSITY
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 1998
• 资助国家: 日本
• 起止时间: 1998 至 1999
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10680396/
• 关键词: Search space boundary extension method; Boundary extension by mirroring; Search function division GAs; Multi-parent recombination; Real coded GAs; Genetic Algorithms (GAs); Boundary extension by special selection; Simplex crossover; 機能分割型分散GA; 複数

As early exploration of the search space gradually results in exploitation of a small number of fit regions later on in the search process, conventional genetic algorithms (GAs) tend to face difficulty in solving certain kinds of problem, such as highly multimodal functions, functions with high levels of epistasis , and deceptive features. On such problems, typical premature convergence occurs in conventional GAs, before the early exploration stage has had a chance to set the population in the right direction.To cope with this problem in genetic search, we proposed a "function division scheme" in GAs. In this scheme, we use separate populations for exploration and exploitation ; one of these populations, the explorer sub-GA, tries mainly to explore the whole search space and maintain a useful degree of global diversity, while the other population, the exploiter sub-GA, exploits the neighborhood of the best solution obtained so far. Thus the search function of the algorithm is divided into two functions, explorer and exploiter functions. The effectiveness of this scheme has been shown using several test functions. Further, we proposed several genetic operators that are useful for this scheme. They include multi-parent recombination operators and search space boundary extension operators.

由于搜索空间的早期探索逐渐导致在搜索过程后期利用少量的适合区域，传统的遗传算法（GAs）在解决某些类型的问题时往往面临困难，例如高度多峰函数，具有高水平的上位性的函数，以及欺骗性的功能。在这类问题中，传统遗传算法在早期探索阶段还没有机会将种群调整到正确的方向之前，会出现典型的早熟收敛现象，为了科普这一问题，我们提出了一种遗传算法中的“功能划分方案”。在该方案中，我们使用单独的人口进行探索和利用;这些人口之一，探险家子GA，主要是试图探索整个搜索空间，并保持一个有用的程度的全球多样性，而其他人口，剥削者子GA，利用迄今为止获得的最佳解决方案的邻域。该算法的搜索功能分为两个功能，探索者和开发者功能。该方案的有效性已被证明使用几个测试功能。此外，我们提出了几个遗传算子，是有用的，这个计划。它们包括多父重组算子和搜索空间边界扩展算子。

项目成果

S. Tsutsui and L. C. Jain: "Properties of Robust Solution Searching in Multi-dimensional Space with Genetic Algorithms"Proceeding of the 2nd International Conference on Knowledge-based Intelligent Electronic Systems (KES'98). 3. 161-167 (1998)

S. Tsutsui 和 L. C. Jain：“利用遗传算法在多维空间中搜索鲁棒解决方案的属性”第二届基于知识的智能电子系统国际会议 (KES98) 论文集。

S.Tsutsui,M.Yamamura,and T.Higuchi: "Multi-parent Recombination with Simplex Crossover in Real-Coded Genetic Algorithms"Proc.Of the Genetic and Evolutionary Computation Conference(GECCO-99). Vol.1. 657-664 (1999)

S.Ttsutsui、M.Yamamura 和 T.Higuchi：“实编码遗传算法中的多亲重组与单纯交叉”Proc.Of the Genetic and Evolutionary Computation Conference (GECCO-99)。

筒井茂義: "新世代工学シリーズ 人工知能,第5.3節「遺伝的アルゴリズム」"オーム社. 8 (2000)

Shigeyoshi Tsutsui：“新一代工程系列人工智能，第 5.3 节“遗传算法”” Ohmsha 8 (2000)。

S.Tsutsui amd A.Ghosh: ""Robust Solution Search by Genetic Algorithms"in Advances in Evolutionary Computation"Springer(2000/12予定).

S. Tsutsui 和 A. Ghosh：《进化计算进展》中的“Robust Solution Search by Genetic Algorithms” Springer（计划于 2000 年 12 月）。

S.Tsutsui,L.C.Jain: "Properties of Robust Solution Searching in Multi-dimensional space with Genetic Algorichms" Proc.of the 2nd International Conferene on Knowledge-based Intelligent Electronic Systems. 161-167 (1998)

S.Tsutsui，L.C.Jain：“利用遗传算法在多维空间中进行鲁棒解搜索的特性”第二届国际基于知识的智能电子系统会议的会议记录。