A Linear Constrained Optimization Benchmark for Probabilistic Search Algorithms: The Rotated Klee-Minty Problem

被引：1

作者：

Hellwig, Michael ^{[1
]}

Beyer, Hans-Georg ^{[1
]}

机构：

[1] Vorarlberg Univ Appl Sci, Res Ctr PPE, Campus 5,Hoch Str 1, A-6850 Dornbirn, Austria

来源：

THEORY AND PRACTICE OF NATURAL COMPUTING (TPNC 2018) | 2018年 / 11324卷

基金：

奥地利科学基金会;

关键词：

Probabilistic search algorithms; Benchmarking; Evolutionary algorithms; Linear problems; Constrained optimization; Klee-Minty cube;

D O I：

10.1007/978-3-030-04070-3_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The development, assessment, and comparison of randomized search algorithms heavily rely on benchmarking. Regarding the domain of constrained optimization, the small number of currently available benchmark environments bears no relation to the vast number of distinct problem features. The present paper advances a proposal of a scalable linear constrained optimization problem that is suitable for benchmarking Evolutionary Algorithms. By comparing two recent Evolutionary Algorithm variants, the linear benchmarking environment is demonstrated.

引用

页码：139 / 151

页数：13

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