Baldwinian learning in clonal selection algorithm for optimization

Gong, Maoguo; Jiao, Licheng; Zhang, Lining

doi:10.1016/j.ins.2009.12.007

Cited by 94 publications

(57 citation statements)

References 41 publications

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“…1, we can notice that the learning mechanisms used in (1)-(6) on the antibody Ab only utilize random perturbation on the antibody itself, while those in (7)- (8) make use of information in the environment. As reported in (Cutello et al, 2006;Gong et al, 2010), learning from the environment provides an encouraging alternative method, probably a more easy way to achieve better search performance. In details, the mechanism in (7) uses the information of a randomly selected antibodies in the population to Artificial Immune Systems -ICARIS guide the current search.…”

Section: Search Direction Based Learning Operatormentioning

confidence: 84%

“…In order to evaluate the performance of the proposed L sd learning operator, it is validated using some well-known benchmark numerical optimization problems obtained from the literatures (Yao et al, 1999;Cutello et al, 2006;Gong et al, 2010). Table 1 lists the details of the benchmark functions.…”

Section: Resultsmentioning

confidence: 99%

“…• Baldwinian learning L bl (Gong et al, 2010): learning mechanism can provide an easy evolutionary path towards co-adapted alleles in environments, by means of employing differential information during other antibodies. It is realized as…”

Section: Ecal 2013 876mentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Search Direction Based Learning Operatormentioning

confidence: 84%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…From this figure, it is clear that the quality of mutated populations is determined by the capacity of learning operators [31], [40], thus directly influencing the performance of the algorithm. Many researchers have designed and investigated an amount of learning operators, some of which are widely used in evolutionary algorithms [32], [33], while others are specifically designed based on the mechanisms in biological immune systems [34], [36], [41]. It should be noted that it is not our task in this work to make a comprehensive review of all learning operators used in AIS.…”

Section: Learning Operators In Aismentioning

confidence: 99%

“…A well-designed learning operator can not only guide the search to promising areas which are near to the global optima with a high probability, but also reduce the number of useless or redundant search times. In the literature, there are various learning (mutation) operators [31], wherein gaussian mutation [32], cauchy mutation [32], [33], lateral mutation [34], [35] and baldwinian learning [36] are commonly used by many researchers due to their simplicities and easy implementations. However, the efficiencies of these operators are problem-independent.…”

mentioning

confidence: 99%

A Multi-Learning Immune Algorithm for Numerical Optimization

Wang

Gao

Aorigele

et al. 2015

IEICE Trans. Fundamentals

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SUMMARYThe emergence of nature-inspired algorithms (NIA) is a great milestone in the field of computational intelligence community. As one of the NIAs, the artificial immune algorithm (AIS) mimics the principles of the biological immune system, and has exhibited its effectiveness, implicit parallelism, flexibility and applicability when solving various engineering problems. Nevertheless, AIS still suffers from the issues of evolution premature, local minima trapping and slow convergence due to its inherent stochastic search dynamics. Much effort has been made to improve the search performance of AIS from different aspects, such as population diversity maintenance, adaptive parameter control, etc. In this paper, we propose a novel multi-learning operator into the AIS to further enrich the search dynamics of the algorithm. A framework of embedding multiple commonly used mutation operators into the antibody evolution procedure is also established. Four distinct learning operators including baldwinian learning, cauchy mutation, gaussian mutation and lateral mutation are selected to merge together as a multi-learning operator. It can be expected that the multi-learning operator can effectively balance the exploration and exploitation of the search by enriched dynamics. To verify its performance, the proposed algorithm, which is called multi-learning immune algorithm (MLIA), is applied on a number of benchmark functions. Experimental results demonstrate the superiority of the proposed algorithm in terms of convergence speed and solution quality.

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Retracted: Sparse data inspired depth of urbanization in Zhejiang based on geographic information system and visual system

Li¹,

Hu²

2020

Concurrency and Computation

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In the recent years, and also considering the rapid development of the urbanization, China has set up the higher requirements for modern urban planning concepts, methods, and means. Under the support of advanced technologies, it has become an inevitable trend to then make the full use of information technology to improve the design and management of city planning. Geographic information system (GIS) is a specific extremely essential spatial information system. It is a complex platform that collects, stores, manages, calculates, analyzes, displays, and describes geographical distribution data in all or part of the Earth's surface (including the atmosphere) space with the proper integration of the information processing and computer vision algorithms.In this paper, we apply the GIS and multimedia information systems to study the evaluation of local urbanization in Zhejiang. The multimedia system is designed to assist the GIS and the visualized model is presented to validate the proposed method. The results prove that the proposed is efficient.

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Baldwinian learning in clonal selection algorithm for optimization

Cited by 94 publications

References 41 publications

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

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

A Multi-Learning Immune Algorithm for Numerical Optimization

Retracted: Sparse data inspired depth of urbanization in Zhejiang based on geographic information system and visual system

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