{ "info": { "author": "Simon Wessing", "author_email": "simon.wessing@tu-dortmund.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "Description\n===========\n\nThis package contains a collection of common benchmark problems for black-box\noptimization. Under the term \"black-box problem\", we understand problems for\nwhich only little is known about their structure and properties. Such problems\nusually appear in practice when simulator output or some other complex system\nwith nonlinear behavior is to be optimized.\n\nContained test problems:\n\n* Binary problems OneMax, LeadingOnes, and LeadingOnesTrailingZeros and three\n instance generators for multimodal problems\n* CEC 2005 collection of single-objective problems\n* CEC 2007 collection of multiobjective problems\n* Dixon-Szeg\u00f6 collection for global optimization\n* DTLZ problems 1-7\n* Multiple-Peaks Model 2\n* Walking Fish Group (WFG) toolkit\n* ZDT collection for multiobjective optimization\n\nContained real-world problems:\n\n* Configuration of a gradient method on test problem\n* Uniformity optimization of points in the unit hypercube\n\n\nThe infrastructure of this package can also be used to wrap your own\n(real-world) optimization problems in the problem base class. Reasons to do\nthis may be the following features:\n\n* Support for single-objective and multi-objective problems\n* In general, no assumptions about the search space are made\n* Evaluations are automatically counted\n* Can use true parallelism or concurrency via multiprocessing(.dummy)\n* Provides functionality for checking bound constraints and repairing\n violations of them in continuous optimization\n* Optionally: detection and subsequently avoidance of duplicate evaluations\n\n\nDependencies\n============\n\nSome modules of this package have additional dependencies on third-party\npackages. 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