Immune Algorithm Versus Differential Evolution: A Comparative Case Study Using High Dimensional Function Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007-01-01

AUTHORS

Vincenzo Cutello , Natalio Krasnogor , Giuseppe Nicosia , Mario Pavone

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 IA we 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. More... »

PAGES

93-101

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-71618-1_11

DOI

http://dx.doi.org/10.1007/978-3-540-71618-1_11

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1048456269


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