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

  • Vincenzo Cutello
  • Natalio Krasnogor
  • Giuseppe Nicosia
  • Mario Pavone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4431)


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.


Memetic Algorithm Benchmark Function Immune Algorithm Crossover Scheme Classical Cloning 
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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Vincenzo Cutello
    • 1
  • Natalio Krasnogor
    • 2
  • Giuseppe Nicosia
    • 1
  • Mario Pavone
    • 1
  1. 1.Department of Mathematics and Computer Science, University of Catania, Viale A. Doria 6, 95125 CataniaItaly
  2. 2.Automated Scheduling, Optimisation and Planning Research Group, School of Computer Sciences and IT, Jubilee Campus, University of Nottingham, Nottingham, NG81BBUK

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