Paper Info
Title
Building algorithm portfolios for memetic algorithms
Abstract
The present study introduces an automated mechanism to build algorithm portfolios for memetic algorithms. The objective is to determine an algorithm set involving combinations of crossover, mutation and local search operators based on their past performance. The past performance is used to cluster algorithm combinations. Top performing combinations are then considered as the members of the set. The set is expected to have algorithm combinations complementing each other with respect to their strengths in a portfolio setting. In other words, each algorithm combination should be good at solving a certain type of problem instances such that this set can be used to solve different problem instances. The set is used together with an online selection strategy. An empirical analysis is performed on the Quadratic Assignment problem to show the advantages of the proposed approach.
Year
DOI
Venue
2014
10.1145/2598394.2598455
GECCO (Companion)
Keywords
Field
DocType
problem solving, control methods, and search,genetic programming,evolutionary algorithms
Memetic algorithm,Evolutionary algorithm,Computer science,Genetic programming,Operator (computer programming),Artificial intelligence,Mathematical optimization,Crossover,Quadratic assignment problem,Algorithm,Probabilistic analysis of algorithms,Local search (optimization),Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
3
Name
Order
Citations
PageRank
Mustafa Misir151.76
Stephanus Daniel Handoko2959.18
Hoong Chuin Lau373991.69