Software review: DEAP (Distributed Evolutionary Algorithm in Python']Python) library

被引：17

作者：

Kim, Jinhan ^{[1
]}

Yoo, Shin ^{[1
]}

机构：

[1] Korea Adv Inst Sci & Technol, Sch Comp, 291 Daehak Ro, Daejeon 34141, South Korea

来源：

GENETIC PROGRAMMING AND EVOLVABLE MACHINES | 2019年 / 20卷 / 01期

基金：

新加坡国家研究基金会;

关键词：

Data handling - Genetic algorithms - Genetic programming - Learning systems - Open source software - High level languages - Computer software selection and evaluation;

D O I：

10.1007/s10710-018-9341-4

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We give a critical assessment of the DEAP (Distributed Evolutionary Algorithm in Python) open-source library and highly recommend it to both beginners and experts alike. DEAP supports a range of evolutionary algorithms including both strongly and loosely typed Genetic Programming, Genetic Algorithm, and Multi-Objective Evolutionary Algorithms such as NSGA-II and SPEA2. It contains most of the basic functions required by evolutionary computation, so that its users can easily construct various flavours of both single and multi-objective evolutionary algorithms and execute them using multiple processors. It is ideal for fast prototyping and can be used with an abundance of other Python libraries for data processing as well as other machine learning techniques.

引用

页码：139 / 142

页数：4

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