Multimodal Dynamic Optimization: From Evolutionary Algorithms to Artificial Immune Systems

Nanas, Nikolaos; Roeck, Anne De

doi:10.1007/978-3-540-73922-7_2

Cited by 29 publications

(12 citation statements)

References 14 publications

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“…Figs. [16][17][18] show in the x-axis the ID for a combination of parameter values (corresponding to Table 5) and the y-axis indicates the offline errors for each environment. The size of MC does not produce significant differences, when the rest of the parameters remain fixed.…”

Section: Discussionmentioning

confidence: 99%

“…Nanas and De Roeck [18] compared several evolutionary algorithms and artificial immune systems that have been used to solve multimodal dynamic optimization problems. They reviewed several basic evolutionary and immune-inspired approaches available to solve multimodal dynamic optimization problems and identified correspondences and differences among them.…”

Section: Previous Related Workmentioning

confidence: 99%

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A T-cell algorithm for solving dynamic optimization problems

Aragón

Esquivel

Coello

2011

Information Sciences

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Section: Discussionmentioning

confidence: 99%

Section: Previous Related Workmentioning

confidence: 99%

A T-cell algorithm for solving dynamic optimization problems

Aragón

Esquivel

Coello

2011

Information Sciences

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“…They may lose diversity as the optimum solution proliferates and spreads over the population. By enabling diversity in the population by means of CSA, this drawback is attempted to be solved (Nanas and De Roeck 2007). CSA is used to improve local search and to avoid premature convergence in PSO by Wang et al (2006a;2006c), Mitra and Venayagamoorthy (2008), and Hong (2009).…”

Section: Hybrid Csa Methods and Applicationsmentioning

confidence: 99%

A review of clonal selection algorithm and its applications

2011

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Recently, clonal selection theory in the immune system has received the attention of researchers and given them inspiration to create algorithms that evolve candidate solutions by means of selection, cloning, and mutation procedures. Moreover, diversity in the population is enabled by means of the receptor editing process. The Clonal Selection Algorithm (CSA) in its canonical form and its various versions are used to solve different types of problems and are reported to perform better compared with other heuristics (i.e., genetic algorithms, neural networks, etc.) in some cases, such as function optimization and pattern recognition. Although the studies related with CSA are increasingly popular, according to our best knowledge, there is no study summarizing the basic features of these algorithms, hybrid algorithms, and the application areas of these algorithms all in one paper. Therefore, this study aims to summarize the powerful characteristics and general review of CSA. In addition, CSA based hybrid algorithms are reviewed, and open research areas are discussed for further research.

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“…Diversity of the adaptive immune system is maintained through heterostasis, in which cells are selected to clone or to become memory cells according to antigen affinity [13]. Diversity is extended through heterogenesis [13], either through somatic hypermutation or through recruitment of new cells and the suppression of similar antibodies [2].…”

Section: A Odiss Meta-probes Applied To Aspects Of An Immune Systemmentioning

confidence: 99%

Profile Adaptation in Adaptive Information Filtering: An Immune Inspired Approach

Azmi

Timmis

Polack

2009

2009 International Conference of Soft Computing and Pattern Recognition

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Abstract-Within the context of information filtering, learning and adaptation of user profiles is a challenging research area and is, in part, addressed by work in Adaptive Information Filtering (AIF). In order to be effective in a dynamic context, maintaining filtering performance, information filtering systems need to adapt to changes. We argue that artificial immune systems (AIS) exhibit the properties required by AIF, and have the potential to be exploited in the context of AIF. In this paper, we extract general features of immune systems and AIF, based on a principled meta-probe approach. We then propose an architecture for AIF incorporating ideas from AIS. Having such characteristics as adaptability, diversity and self-organised, we argue that AIS have suitable characteristics that are amenable to the task of AIF.

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Multimodal Dynamic Optimization: From Evolutionary Algorithms to Artificial Immune Systems

Cited by 29 publications

References 14 publications

A T-cell algorithm for solving dynamic optimization problems

A T-cell algorithm for solving dynamic optimization problems

A review of clonal selection algorithm and its applications

Profile Adaptation in Adaptive Information Filtering: An Immune Inspired Approach

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