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In this research paper we present an immunological algorithm (IA) to solve
global numerical optimization problems for highdimensional instances. Such optimization
problems are a crucial component for many realworld applications. We designed two versions
of the IA: the first based on binarycode representation and the second based on real
values, called optIMMALG01 and optIMMALG, respectively. A large set of experiments
is presented to evaluate the effectiveness of the two proposed versions of IA. Both opt
IMMALG01 and optIMMALG were extensively compared against several nature inspired
methodologies including a set of Differential Evolution algorithms whose performance is
known to be superior to many other bioinspired and deterministic algorithms on the same
test bed. Also hybrid and deterministic global search algorithms (e.g., DIRECT, LeGO,
PSwarm) are compared with both IA versions, for a total 39 optimization algorithms.The
results suggest that the proposed immunological algorithm is effective, in terms of accuracy,
and capable of solving largescale instances for wellknown benchmarks. Experimental
results also indicate that both IA versions are comparable, and often outperform, the stateof
theart optimization algorithms.
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