Computational Complexity Analysis of Multi-Objective Genetic Programming

被引：16
|
作者：
Neumann, Frank [1 ]
机构：
[1] Univ Adelaide, Sch Comp Sci, Adelaide, SA 5005, Australia
来源：
PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE | 2012年
关键词：
Genetic Programming; Mutation; Multi-Objective Optimization; Theory; Runtime Analysis; 1+1 EVOLUTIONARY ALGORITHM;
D O I：
10.1145/2330163.2330274
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The computational complexity analysis of genetic programming (GP) has been started recently in [7] by analyzing simple (1 + 1) GP algorithms for the problems ORDER and MAJORITY. In this paper, we study how taking the complexity as an additional criteria influences the runtime behavior. We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1 + 1) GP using multi-criteria fitness functions that take into account the original objective and the complexity of a syntax tree as a secondary measure. Furthermore, we study the expected time until population-based multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important objective.
引用
收藏
页码：799 / 806
页数：8
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