2016/04/06 by Mojgan Pourhassan, Feng Shi, Pourhassan, Mojgan +3
Computer Science · #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Neural and Evolutionary Computing (cs.NE) #cs.DS #cs.NE
paper · pdf · doi:10.48550/arxiv.1604.01495
arxiv created 2016/04/06 · arxiv updated 2016/04/07
A rigorous runtime analysis of evolutionary multi-objective optimization for the classical vertex cover problem in the context of parameterized complexity analysis has been presented by Kratsch and Neumann (2013). In this paper, we extend the analysis to the weighted vertex cover problem and provide a fixed parameter evolutionary algorithm with respect to OPT, the cost of the the optimal solution for the problem. Moreover, using a diversity mechanisms, we present a multi-objective evolutionary algorithm that finds a 2-approximation in expected polynomial time and introduce a population-based evolutionary algorithm which finds a (1+ε)-approximation in expected time O(n⋅ 2^min \n,2(1- ε)OPT \ + n3).