Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators

Cutello, Vincenzo; Nicosia, Giuseppe; Pavone, Mario

doi:10.1007/978-3-540-30220-9_22

Cited by 105 publications

(90 citation statements)

References 7 publications

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“…In this research paper two well known CSAs are analyzed: CLONal selection ALGorithm (CLONALG) [4] and optimization Immune Algorithm (opt-IA) [5], which both use a simplified model of the Clonal Selection Principle . To analyze experimentally the overall performance of those two algorithms, we will test them on a robust set of problems belonging to four different classes: toy problems, pattern recognition, numerical optimization problems and NP-complete problems.…”

Section: Introductionmentioning

confidence: 99%

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Cutello

Narzisi

Nicosia

et al. 2005

Lecture Notes in Computer Science

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Abstract. This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks.

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Section: Introductionmentioning

confidence: 99%

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Cutello

Narzisi

Nicosia

et al. 2005

Lecture Notes in Computer Science

Self Cite

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“…• Static Hypermutation L h1 (Cutello et al, 2004;Gong et al, 2008): the number of mutations is independent from the affinity of the antibody. That is to say, Ab ′ will undergo a constant number c of mutation times.…”

Section: Ecal 2013 876mentioning

confidence: 99%

“…• Proportional Hypermutation L h2 (Cutello et al, 2004): the number of mutations is proportional to the normalized affinity value, that is,f (Ab) × c × D, wheref (Ab) is the normalized affinity distributed in the interval of [0,1]. c is a constant number, representing the maximum mutation intensity.…”

Section: Ecal 2013 876mentioning

confidence: 99%

“…• Inversely proportional hypermutation L h3 (Cutello et al, 2004(Cutello et al, , 2005(Cutello et al, , 2006: the number of mutations is inversely proportional to the normalized affinity value, i.e., the higher affinity of an antibody, the less times of mutations will be carried out on it. It is reasonable to make such an inverse choice, since better antibodies usually contain more useful information for evolution.…”

Section: Ecal 2013 876mentioning

confidence: 99%

“…• Hypermacromutation L m (Cutello et al, 2004): the number of mutations is independent from the affinity and the parameter c. Instead, the operator mutates at most j −i+1 values in the interval of [i, j], where two randomly generated integers i and j satisfy the condition of i < j ≤ D.…”

Section: Ecal 2013 876mentioning

confidence: 99%

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Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

Gao¹,

Gong²,

Zhong³

et al. 2013

Advances in Artificial Life, ECAL 2013

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Immunological algorithms are a kind of bio-inspired intelligence methods which draw inspiration from natural immune systems. The problem-solving performance of immunological algorithms mainly lies on the utilization of learning (i.e. mutation) operators. In this paper, nine different learning operators in a standard immune algorithmic framework are investigated. These learning operators consist of eight existing operators and a newly proposed search direction based operator. Experiments are conducted based on nine variants of immunological algorithms that use different learning operators. Simulation results on a large number of benchmark optimization problems give a deep insight into the characteristics of these operators, and further verify that the proposed new learning operator can greatly improve the performance of immunological algorithms.

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Bio‐inspired algorithms for the optimization of offshore oil production systems

Vieira

Lima

Jacob

2012

Numerical Meth Engineering

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SUMMARY This work describes the development, implementation, and assessment of enhanced variants of three different groups of bio‐inspired methodologies: genetic algorithms, particle swarm optimization, and artificial immune system. The algorithms are implemented on a computational tool for the synthesis and optimization of offshore oil production risers that connect a floating platform at the sea surface to the wellheads at the sea bottom. Optimization procedures using bio‐inspired algorithms for such real‐world engineering problems require the calculation of the objective function through a large number of time‐consuming finite element nonlinear dynamic analyses, for the evaluation of the structural behavior of each candidate configuration. Therefore, the performance of the algorithms may be measured by the smaller number of objective function evaluations associated to a given target fitness value. The results indicate that the artificial immune system approach, incorporating some enhancements presented in this work, is more effective than the genetic algorithms and particle swarm optimization methods, requiring a smaller number of evaluations to obtain better solutions. Copyright © 2012 John Wiley & Sons, Ltd.

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Exploring the Capability of Immune Algorithms: A Characterization of Hypermutation Operators

Cited by 105 publications

References 7 publications

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Clonal Selection Algorithms: A Comparative Case Study Using Effective Mutation Potentials

Enhancing the learning capacity of immunological algorithms: a comprehensive study of learning operators

Bio‐inspired algorithms for the optimization of offshore oil production systems

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