New Solutions for the Density Classification Task in One Dimensional Cellular Automata

Laboudi, Zakaria; Chikhi, Salim

doi:10.1007/978-3-030-05481-6_7

Cited by 2 publications

(2 citation statements)

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“…Instead of operating over a complete solution space (as presented, for instance, in the references [7,8,12,16,19,37]), the method presented here focuses on a limited set of CA evolution rules (specifically nine rules, in Table 2 and Figure 9) and only six combinations thereof (refer to Figure 9), where optimal solutions are located. Consequently, this streamlines the design process, making it more straightforward and more efficient.…”

Section: Discussionmentioning

confidence: 99%

“…These approaches were the key to the discovery of a significant number of solutions, especially with the introduction of the symmetry property [12][13][14], with the introduction of the conserving property [15,16], asynchronous updating methods [17], and even firing-squad synchronization that aims to establish an evolution rule whereby all cells within a given region enter into a special state after an identical number of steps [18] (p. 1035). Another interesting approach is presented in [19], where symmetry and conserving properties are achieved using CA with neighborhood dimension r = 4.…”

Section: Introductionmentioning

confidence: 99%

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Evolution of Hybrid Cellular Automata for Density Classification Problem

Anghelescu

2024

Symmetry

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This paper describes a solution for the image density classification problem (DCP) using an entirely distributed system with only local processing of information named cellular automata (CA). The proposed solution uses two cellular automata’s features, density conserving and translation of the information stored in the cellular automata’s cells through the lattice, in order to obtain the solution for the density classification problem. The motivation for choosing a bio-inspired technique based on CA for solving the DCP is to investigate the principles of self-organizing decentralized computation and to assess the capabilities of CA to achieve such computation, which is applicable to many real-world decentralized problems that require a decision to be taken by majority voting, such as multi-agent holonic systems, collaborative robots, drones’ fleet, image analysis, traffic optimization, forming and then separating clusters with different values. The entire application is coded using the C# programming language, and the obtained results and comparisons between different cellular automata configurations are also discussed in this research.

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