NEW EVOLUTIONARY GENETIC ALGORITHMS FOR NP-COMPLETE COMBINATORIAL OPTIMIZATION PROBLEMS

被引：35

作者：

BAC, FQ

PEROV, VL

机构：

[1] Mendeleev Institute of Chemical Engineering, Moscow, 123480, ul. Vilisa Lasisa

来源：

BIOLOGICAL CYBERNETICS | 1993年 / 69卷 / 03期

关键词：

D O I：

10.1007/BF00198963

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Evolutionary genetic algorithms have been proposed to solve NP-complete combinatorial optimization problems. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithms were described as stochastic processes, using population dynamics and interactive markovian chains. The proposed algorithms were used in solving flowshop problems and an asymmetric traveling salesman problem. The experimental results showed the superiority of new evolutionary algorithms in comparison with the standard genetic algorithm.

引用

页码：229 / 234

页数：6

共 16 条

[1] THE MOLECULAR TRAVELING SALESMAN [J].