Pflacco: Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems in Python.

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolutionary Computation Pub Date : 2024-09-03 DOI:10.1162/evco_a_00341
Raphael Patrick Prager, Heike Trautmann
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引用次数: 0

Abstract

The herein proposed Python package pflacco provides a set of numerical features to characterize single-objective continuous and constrained optimization problems. Thereby, pflacco addresses two major challenges in the area of optimization. Firstly, it provides the means to develop an understanding of a given problem instance, which is crucial for designing, selecting, or configuring optimization algorithms in general. Secondly, these numerical features can be utilized in the research streams of automated algorithm selection and configuration. While the majority of these landscape features are already available in the R package flacco, our Python implementation offers these tools to an even wider audience and thereby promotes research interests and novel avenues in the area of optimization.

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Pflacco:用 Python 对连续和受限优化问题进行基于特征的景观分析
本文提出的 Python 软件包 pflacco 提供了一组数值特征,用于描述单目标连续和约束优化问题。因此,pflacco 解决了优化领域的两大难题。首先,它提供了理解给定问题实例的方法,这对于设计、选择或配置一般优化算法至关重要。其次,这些数字特征可用于自动算法选择和配置的研究流。虽然这些景观特征中的大部分已在 R 软件包 flacco 中提供,但我们的 Python 实现为更广泛的受众提供了这些工具,从而促进了优化领域的研究兴趣和新途径。
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来源期刊
Evolutionary Computation
Evolutionary Computation 工程技术-计算机:理论方法
CiteScore
6.40
自引率
1.50%
发文量
20
审稿时长
3 months
期刊介绍: Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.
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