Progress properties and fitness bounds for geometric semantic search operators

详细信息    查看全文

• 作者：Tomasz P. Pawlak ; Krzysztof Krawiec
• 关键词：Geometric semantic genetic programming ; Theory ; Metric ; Fitness landscape ; Fitness bounds ; Guarantees of progress
• 刊名：Genetic Programming and Evolvable Machines
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：17
• 期：1
• 页码：5-23
• 全文大小：645 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Software Engineering, Programming and Operating Systems
  Electronic and Computer Engineering
  Programming Techniques
  Programming Languages, Compilers and Interpreters
  
• 出版者：Springer Netherlands
• ISSN：1573-7632

文摘

Metrics are essential for geometric semantic genetic programming. On one hand, they structure the semantic space and govern the behavior of geometric search operators; on the other, they determine how fitness is calculated. The interactions between these two types of metrics are an important aspect that to date was largely neglected. In this paper, we investigate these interactions and analyze their consequences. We provide a systematic theoretical analysis of the properties of abstract geometric semantic search operators under Minkowski metrics of arbitrary order. For nine combinations of popular metrics (city-block, Euclidean, and Chebyshev) used in fitness functions and of search operators, we derive pessimistic bounds on fitness change. We also define three types of progress properties (weak, potential, and strong) and verify them for operators under those metrics. The analysis allows us to determine the combinations of metrics that are most attractive in terms of progress properties and deterioration bounds.