GP vs GI: If You Can't Beat Them, Join Them

被引：2

作者：

Woodward, John R. ^{[1
]}

Johnson, Colin G. ^{[2
]}

Brownlee, Alexander E. I. ^{[1
]}

机构：

[1] Univ Stirling, Stirling, Scotland

[2] Univ Kent, Canterbury CT2 7NZ, Kent, England

来源：

PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION) | 2016年

基金：

英国工程与自然科学研究理事会;

关键词：

Genetic Improvement (GI); Genetic Programming (GP);

D O I：

10.1145/2908961.2931694

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Genetic Programming (GP) has been criticized for targeting irrelevant problems [12], and is also true of the wider machine learning community [11]. which has become detached from the source of the data it is using to drive the field forward. However, recently GI provides a fresh perspective on automated programming. In contrast to GP, GI begins with existing software, and therefore immediately has the aim of tackling real software. As evolution is the main approach to GI to manipulating programs, this connection with real software should persuade the GP community to confront the issues around what it originally set out to tackle i.e. evolving real software.

引用

页码：1155 / 1156

页数：2

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