A symbiosis between cellular automata and genetic algorithms

We propose an approach to self-optimizing wireless sensor networks (WSNs) which are able to find, in a fully distributed way, a solution to a coverage and lifetime optimization problem. The proposed approach is based on three components: (a) a multi-agent, social-like interpreted system, where the modeling of agents, discrete space, and time is provided by a 2-dimensional second-order cellular automata, (b) the interaction between agents is described in terms of the spatial prisoner’s dilemma game, and (c) a local evolutionary mechanism of competition between agents exists. Nodes of a WSN graph created for a given deployment of WSN in the monitored area are considered agents of a multi-agent system that collectively make decisions to turn on or turn off their batteries. Agents are controlled by cellular automata (CA)-based players participating in a variant of the spatial prisoner’s dilemma iterated game. We propose for players participating in this game a local payoff function that incorporates issues of area coverage and sensors energy spending. Rewards obtained by agent players depend not only on their personal decisions but also on their neighbor’s decisions. Agents act in such a way to maximize their own rewards, which results in achieving by them a solution corresponding to the Nash equilibrium point. We show that the system is self-optimizing, i.e., can optimize in a distributed way global criteria related to WSN and not known for agents, provide a balance between requested coverage and spending energy, and result in expanding the WSN lifetime. The solutions proposed by the multi-agent system fulfill the Pareto optimality principles, and the desired quality of solutions can be controlled by user-defined parameters. The proposed approach is validated by a number of experimental results.

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Section: Related Workmentioning

confidence: 99%

“…In this context, it is also worth noticing the works [46,47]. In [46], the authors combine 2D CA cells with concepts of GA, allowing evolving CA cell rules due to local mating into a desired direction. The authors of [47] combine game theory concepts with GA concepts, which results in designing very effective GA operators.…”

Section: Related Workmentioning

confidence: 99%

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

Seredyński

Kulpa

Hoffmann

et al. 2023

Sensors

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“…In order to ensure the accuracy of retrieval, in the process of retrieving portal information under massive data, it is necessary to extract relevant features of the portal information, and the features of the portal information usually consist of multiple dimensions, which will make the process of retrieving portal information too complicated and complex, which reduces the accuracy of retrieving web portal information [7]. Since the portal website information dimension contains a lot of redundant data [8], it is necessary to reduce the portal website information dimension and retain the main feature information to eliminate the influence of redundant data.…”

Section: Calculation Of Information Relevance Of Dynamic Webmentioning

confidence: 99%

Fast Retrieval Method of Portal Information Based on a Chaotic Genetic Algorithm

Zhao

Tai

2022

Mathematical Problems in Engineering

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The traditional retrieval method cannot respond to the influence of the change in the portal website’s information characteristics, resulting in low efficiency. In this regard, a fast information retrieval method based on a chaotic genetic algorithm is proposed. According to the relevant theory of association rules, the correlation between information data of dynamic portal websites is calculated; different portal website information is retrieved based on the Markov model output; a chaotic genetic algorithm is used to fuse different portal website information. The information data constructs a decision tree for rapid retrieval of portal information, uses the vector form to express the characteristics of portal information, and finally realizes the rapid retrieval of portal information. The experimental results show that the designed method takes up to 15 ms when the sample complexity is high, which shows that the designed method has high efficiency and is of great significance in practical applications.

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“…In [16], Ameyalli's rule, a new two-dimensional CA, is described, featuring an emergent glider gun used to construct logic gates for logical universality. A new system mixing a CA and a genetic algorithm, showing interesting behaviors and glider production, is presented in [17]. Isotropic CAs capable of producing glider-gun and eater dynamics are explained in [18].…”

Section: Introductionmentioning

confidence: 99%

Mean-Field Analysis with Random Perturbations to Detect Gliders in Cellular Automata

Seck-Tuoh-Mora,

Medina-Marin,

Hernández-Romero

et al. 2023

Mathematics

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Cellular automata are mathematical models that represent systems with complex behavior through simple interactions between their individual elements. These models can be used to study unconventional computational systems and complexity. One notable aspect of cellular automata is their ability to create structures known as gliders, which move in a regular pattern to represent the manipulation of information. This paper introduces the modification of mean-field theory applied to cellular automata, using random perturbations based on the system’s evolution rule. The original aspect of this approach is that the perturbation factor is tailored to the nature of the rule, altering the behavior of the mean-field polynomials. By combining the properties of both the original and perturbed polynomials, it is possible to detect when a cellular automaton is more likely to generate gliders without having to run evolutions of the system. This methodology is a useful approach to finding more examples of cellular automata that exhibit complex behavior. We start by examining elementary cellular automata, then move on to examples of automata that can generate gliders with more states. To illustrate the results of this methodology, we provide evolution examples of the detected automata.

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A symbiosis between cellular automata and genetic algorithms

Cited by 11 publications

References 37 publications

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

Coverage and Lifetime Optimization by Self-Optimizing Sensor Networks

Fast Retrieval Method of Portal Information Based on a Chaotic Genetic Algorithm

Mean-Field Analysis with Random Perturbations to Detect Gliders in Cellular Automata

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