Cooperating swarms: A paradigm for collective intelligence and its application in finance.

Mukhopadhyay, Sumona; Banerjee, Santo

doi:10.5120/1107-1450

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…First, various literature studies explore the phenomenon of collective intelligence from the perspective of its emergence in animal communities, such as ant colonies [20,41], bee swarms and fish flocks. The observations made on these communities, have inspired some of the most wide-known algorithms for solving and optimizing complex computational problems [11,30,34].…”

Section: Related Literaturementioning

confidence: 99%

Collective Intelligence Systems: Classification and Modeling

Lykourentzou¹,

Vergados²,

Kapetanios³

et al. 2011

JETWI

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Collective intelligence (CI) is an emerging research field that seeks to merge human and machine intelligence, with an aim to achieve results unattainable by either one of these entities alone. CI systems may significantly vary in nature, from collaborative systems, like open source software development communities, to competitive ones, like problem-solving companies that benefit from the competition among participating user teams to identify solutions to various R&D problems. The advantages that CI systems earn user communities, together with the fact that they share a number of basic common features, provide the potential for designing a general methodology for their efficient modeling, development and evaluation. In this paper we describe a modeling process which identifies the common features, as well as the main challenges that the construction of generic collective intelligence systems poses. First a basic categorization of CI systems is performed, followed by a description of the proposed modeling approach. This approach includes concepts such as the set of possible user actions, the CI system state and the individual and community objectives, as well as a number of necessary functions, which estimate various parameters of the CI system, such as the expected user actions, the future system state and the level of objective fulfillment. Finally, based on the proposed modeling approach, certain current CI systems are described, a number of problems that they face are identified and specific solutions are suggested. The proposed modeling approach is expected to promote more efficient CI system design, so that the benefit gained by the participating community and individuals, will be maximized.

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Section: Related Literaturementioning

confidence: 99%

Collective Intelligence Systems: Classification and Modeling

Lykourentzou¹,

Vergados²,

Kapetanios³

et al. 2011

JETWI

View full text Add to dashboard Cite

show abstract

“…Such characteristics are rooted in the propensity of PSO algorithms to lose the diversity of the swarm, leading to premature convergence and leaving many areas of the search space unexplored. To address these problems, different solutions including Algorithm variants, such as chaotic PSO [12], fuzzy PSO [13], among others [14][15][16], as well as Algorithm modifications including constriction coefficient [17], velocity clamping [18], among others [19][20][21][22][23], have been proposed. Furthermore, Algorithm hybridization techniques, where PSO is combined with other evolutionary computation approaches such as genetic algorithms [24], ant colony optimization [25] and differential evolution [26] have been considered as well.…”

Section: Introductionmentioning

confidence: 99%

A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data

Dhrif,

Giraldo,

Kubat

et al. 2019

Preprint

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Evolutionary computation (EC) algorithms, such as discrete and multi-objective versions of particle swarm optimization (PSO), have been applied to solve the Feature selection (FS) problem, tackling the combinatorial explosion of search spaces that are peppered with local minima. Furthermore, highdimensional FS problems such as finding a small set of biomarkers to make a diagnostic call add an additional challenge as such methods ability to pick out the most important features must remain unchanged in decision spaces of increasing dimensions and presence of irrelevant features. We developed a combinatorial PSO algorithm, called COMB-PSO, that scales up to high-dimensional gene expression data while still selecting the smallest subsets of genes that allow reliable classification of samples. In particular, COMB-PSO enhances the encoding, speed of convergence, control of divergence and diversity of the conventional PSO algorithm, balancing exploration and exploitation of the search space. Applying our approach on real gene expression data of different cancers, COMB-PSO finds gene sets of smallest size that allow a reliable classification of the underlying disease classes.

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Hybrid Swarm Intelligence

Gonsalves

2014

Encyclopedia of Information Science and Technology, Third Edition

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Cooperating swarms: A paradigm for collective intelligence and its application in finance.

Cited by 3 publications

References 25 publications

Collective Intelligence Systems: Classification and Modeling

Collective Intelligence Systems: Classification and Modeling

A Stable Combinatorial Particle Swarm Optimization for Scalable Feature Selection in Gene Expression Data

Hybrid Swarm Intelligence

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