IOHexperimenter: Benchmarking Platform for Iterative Optimization Heuristics.

IF 4.6 2区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jacob de Nobel, Furong Ye, Diederick Vermetten, Hao Wang, Carola Doerr, Thomas Bäck
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引用次数: 0

Abstract

We present IOHexperimenter, the experimentation module of the IOHprofiler project. IOHexperimenter aims at providing an easy-to-use and customizable toolbox for benchmarking iterative optimization heuristics such as local search, evolutionary and genetic algorithms, and Bayesian optimization techniques. IOHexperimenter can be used as a stand-alone tool or as part of a benchmarking pipeline that uses other modules of the IOHprofiler environment. IOHexperimenter provides an efficient interface between optimization problems and their solvers while allowing for granular logging of the optimization process. Its logs are fully compatible with existing tools for interactive data analysis, which significantly speeds up the deployment of a benchmarking pipeline. The main components of IOHexperimenter are the environment to build customized problem suites and the various logging options that allow users to steer the granularity of the data records.

IOHexperimenter:迭代优化启发法基准测试平台。
我们介绍 IOHprofiler 项目的实验模块 IOHexperimenter。IOHexperimenter旨在为迭代优化启发式算法(如局部搜索、进化算法、遗传算法和贝叶斯优化技术)的基准测试提供一个易于使用且可定制的工具箱。IOHexperimenter 可作为独立工具使用,也可作为使用 IOHprofiler 环境其他模块的基准测试管道的一部分。IOHexperimenter 为优化问题及其求解器提供了一个高效的接口,同时允许对优化过程进行细粒度记录。其日志与现有的交互式数据分析工具完全兼容,从而大大加快了基准测试管道的部署速度。IOHexperimenter 的主要组件是用于构建定制问题套件的环境,以及允许用户控制数据记录粒度的各种日志选项。
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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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