Learning and optimization using the clonal selection principle

Castro, Leandro Nunes de; Zuben, Fernando J. Von

doi:10.1109/tevc.2002.1011539

Cited by 2,000 publications

(1,028 citation statements)

References 20 publications

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“…In this paper we use the CLONALG version for optimization tasks (except for pattern recognition where we will use the other version proposed in [4]), varying the same parameters (N, n, β, d) plus ρ (not studied in [4]) that controls the shape of the mutation rate with respect to the following two equations:…”

Section: Clonalgmentioning

confidence: 99%

“…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%

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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: Clonalgmentioning

confidence: 99%

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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“…CLONALG is a population based algorithm and its only variation operator is mutation. The main search power of CLONALG relies on this mutation operator and therefore, such hyper-mutation operator is the efficiency deciding factor of this technique [14], [15].…”

Section: B Aismentioning

confidence: 99%

An Improved Independent Component Analysis Algorithm Based on Artificial Immune System

Chen¹,

Lu²

2013

IJMLC

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Abstract-Traditional independent component analysis (ICA) method based on FastICA algorithm faced two main disadvantages. One is that the order of the independent components (ICs) is difficult to be determined and the other is that the FastICA algorithm often leads to local minimum solution, and the suitable source signals are not isolated. To alleviate these problems, an improved ICA algorithm based on artificial immune system (AIS) (called AIS-ICA) is presented. AIS is an attractive heuristic technique and has many advantages over other heuristic techniques such as it can be easily implemented and has great capability of escaping local optimal solutions The basic idea of the proposed AIS-ICA algorithm is to use AIS to determine the separating matrix of ICA. Simulation results from the artificial signal data illustrate the efficiency of the proposed AIS-ICA approach.Index Terms-Independent component analysis, artificial immune system, signal separation, heuristic algorithm.

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“…To investigate the effect of learning operators, a standard immunological algorithm framework (called IA) is utilized (de Castro and Zuben, 2002;Kelsey and Timmis, 2003). I-A evolves a population of antibodies (B cells) towards a global optimum through a process of evaluation, cloning, learning (i.e.…”

Section: Immunological Algorithmmentioning

confidence: 99%

“…To address such problems, bioinspired intelligence algorithms (Da Silva Santos et al, 2010;Gao, 2012) have attracted more and more interest, among which the immunological algorithm (IA) is a particular class of optimization methods inspired by the basic features of adaptive immune response to antigenic stimulus. Most IAs mimic the metaphors of clonal selection principle (de Castro and Zuben, 2002), hypermutation (Freitas and Timmis, 2007), receptor editing (Gao et al, 2007) and lateral interaction effect (Whitbrook et al, 2007), providing a promising search mechanism by exploiting and exploring the solution space in parallel and effectively (Dasgupta et al, 2011). The main unique property of IAs is the utilization of the clonal proliferation, and the clonal selection which returns promising solutions acquired in the learning process.…”

Section: Introductionmentioning

confidence: 99%

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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Learning and optimization using the clonal selection principle

Cited by 2,000 publications

References 20 publications

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

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

An Improved Independent Component Analysis Algorithm Based on Artificial Immune System

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

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