TY - GEN
T1 - A parameterized runtime analysis of randomized local search and evolutionary algorithm for max l-uncut
AU - Jain, Pallavi
AU - Kanesh, Lawqueen
AU - Madathil, Jayakrishnan
AU - Saurabh, Saket
N1 - Funding Information:
The first author acknowledges DST, India for SERB-NPDF fellowship [PDF/2016/003508].
Publisher Copyright:
© 2018 Copyright held by the owner/author(s).
PY - 2018/7/6
Y1 - 2018/7/6
N2 - In the last few years, parameterized complexity has emerged as a new tool to analyze the running time of randomized local search algorithm. However, such analysis are few and far between. In this paper, we do a parameterized running time analysis of a randomized local search algorithm and a (1+1) EA for a classical graph partitioning problem, namely, Max l-Uncut, and its balanced counterpart Max Balanced l-Uncut. In Max l-Uncut, given an undirected graph G = (V, E), the objective is to find a partition of V(G) into l parts such that the number of uncut edges - edges within the parts - is maximized. In the last few years, Max l-Uncut and Max Balanced l-Uncut are studied extensively from the approximation point of view. In this paper, we analyze the parameterized running time of a randomized local search algorithm (RLS) for Max Balanced l-Uncut where the parameter is the number of uncut edges. RLS generates a solution of specific fitness in polynomial time for this problem. Furthermore, we design a fixed parameter tractable randomized local search and a (1 + 1) EA for Max l-Uncut and prove that they perform equally well.
AB - In the last few years, parameterized complexity has emerged as a new tool to analyze the running time of randomized local search algorithm. However, such analysis are few and far between. In this paper, we do a parameterized running time analysis of a randomized local search algorithm and a (1+1) EA for a classical graph partitioning problem, namely, Max l-Uncut, and its balanced counterpart Max Balanced l-Uncut. In Max l-Uncut, given an undirected graph G = (V, E), the objective is to find a partition of V(G) into l parts such that the number of uncut edges - edges within the parts - is maximized. In the last few years, Max l-Uncut and Max Balanced l-Uncut are studied extensively from the approximation point of view. In this paper, we analyze the parameterized running time of a randomized local search algorithm (RLS) for Max Balanced l-Uncut where the parameter is the number of uncut edges. RLS generates a solution of specific fitness in polynomial time for this problem. Furthermore, we design a fixed parameter tractable randomized local search and a (1 + 1) EA for Max l-Uncut and prove that they perform equally well.
KW - (1 + 1) EA
KW - Max Balanced l-Uncut
KW - Max l-Uncut
KW - Randomized Local Search
KW - Running time Analysis
UR - http://www.scopus.com/inward/record.url?scp=85051474559&partnerID=8YFLogxK
U2 - 10.1145/3205651.3205749
DO - 10.1145/3205651.3205749
M3 - Conference contribution
AN - SCOPUS:85051474559
T3 - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
SP - 326
EP - 327
BT - GECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2018 Genetic and Evolutionary Computation Conference, GECCO 2018
Y2 - 15 July 2018 through 19 July 2018
ER -