Paper Info
Title
Immune Algorithm Versus Differential Evolution: A Comparative Case Study Using High Dimensional Function Optimization
Abstract
In this paper we propose an immune algorithm (IA) to solve high dimensional global optimization problems. To evaluate the effectiveness and quality of the IAwe performed a large set of unconstrained numerical optimisation experiments, which is a crucial component of many real-world problem-solving settings. We extensively compare the IA against several Differential Evolution (DE) algorithms as these have been shown to perform better than many other Evolutionary Algorithms on similar problems. The DE algorithms were implemented using a range of recombination and mutation operators combinations. The algorithms were tested on 13 well known benchmark problems. Our results show that the proposed IA is effective, in terms of accuracy, and capable of solving large-scale instances of our benchmarks. We also show that the IA is comparable, and often outperforms, all the DE variants, including two Memetic algorithms.
Year
DOI
Venue
2007
10.1007/978-3-540-71618-1_11
ICANNGA (1)
Keywords
Field
DocType
differential evolution,de algorithm,immune algorithm,proposed ia,immune algorithm versus differential,high dimensional global optimization,crucial component,benchmark problem,memetic algorithm,comparative case study,evolutionary algorithms,de variant,high dimensional function optimization,evolutionary algorithm,global optimization
Memetic algorithm,Mathematical optimization,Evolutionary algorithm,Computer science,Meta-optimization,Algorithm,Differential evolution,Function optimization,Artificial intelligence,Machine learning,Global optimization problem,Mutation operator
Conference
Volume
ISSN
Citations 
4431
0302-9743
4
PageRank 
References 
Authors
0.42
8
4
Name
Order
Citations
PageRank
vincenzo cutello155357.63
Natalio Krasnogor2121385.53
Giuseppe Nicosia347946.53
Mario Pavone421219.41