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在组合领域使用新性搜索合成多样化和歧视性的实例集
Alejandro Marrero1, Eduardo Segredo2, Coromoto León3
1Departamento de Ingeniería Informática y de Sistemas, Universidad de La Laguna, San Cristóbal de La Laguna, Spain amarrerd@ull.edu.es.
Evolutionary computation
|May 7, 2024
概括
为算法选择生成多样化和歧视性的合成实例至关重要. 新性搜索方法有效地创建各种问题实例,在覆盖实例空间和识别性能差距方面超过其他方法.
科学领域:
- 人工智能的人工智能
- 计算智能是一种计算智能.
- 优化优化 优化优化
背景情况:
- 训练算法选择模型需要大量的实例数据,通常很难获得.
- 了解不同问题实例 (算法足迹) 的算法性能对于有效的解决者选择至关重要.
研究的目的:
- 开发一种生成合成实例的方法,这些实例既适合目标算法,又具有多样化的特性.
- 通过单个算法运行,高效地创建大量多样化和歧视实例.
主要方法:
- 使用了一种具有健身功能的新奇性搜索算法,平衡新奇性和性能.
- 使用了两种新性定义:解决者组合中的歧视性表现和实例的特征多样性.
- 该方法在背包和垃圾包装领域进行了评估,与多维现象精英档案 (MAP-Elites) 和纯粹歧视性的进化算法进行了比较.
主要成果:
- 与竞争对手相比,新奇性搜索方法显示了实例空间的优越覆盖.
- 它有效地产生了在目标解决者和其他人之间存在各种性能差距的实例.
- 在Knapsack实例空间之前未被发现的区域创建了新的实例.
结论:
- 提出的新性搜索方法有效地产生多样化和歧视性的合成实例.
- 该方法显示了不同解决者投资组合的稳定性,包括随机,确定和最先进的方法.
- 这种技术提供了一个可概括的解决方案,用于创建具有挑战性和多样化的算法开发和选择问题实例.
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