Bio-Imitation of Mexican Migration Routes to the USA with Slime Mould on 3D Terrains

Adamatzky, Andrew; Mart́ınez, Genaro J.

doi:10.1016/s1672-6529(13)60220-6

Cited by 21 publications

(8 citation statements)

References 33 publications

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“…Physarum migrates into environmentally conformable areas and towards source of nutrients. In [31] we explored this analogy to imitate Mexican migration to USA. We have made a 3D Nylon terrain of USA and placed oat flakes, to act as sources of attractants and nutrients, to ten areas with highest concentration of migrants: New York, Jacksonville, Chicago, Dallas, Houston, Denver, Albuquerque, Phoenix, Los Angeles, San Jose.…”

Section: Mass Migrationmentioning

confidence: 99%

Thirty Seven Things to Do with Live Slime Mould

Adamatzky

2016

Emergence, Complexity and Computation

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Slime mould Physarum polycephalum is a large single cell capable for distributed sensing, concurrent information processing, parallel computation and decentralised actuation. The ease of culturing and experimenting with Physarum makes this slime mould an ideal substrate for real-world implementations of unconventional sensing and computing devices. In the last decade the Physarum became a swiss knife of the unconventional computing: give the slime mould a problem it will solve it. We provide a concise summary of what exact computing and sensing operations are implemented with live slime mould. The Physarum devices range from morphological processors for computational geometry to experimental archeology tools, from self-routing wires to memristors, from devices approximating a shortest path to analog physical models of space exploration.

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Section: Mass Migrationmentioning

confidence: 99%

Thirty Seven Things to Do with Live Slime Mould

Adamatzky

2016

Emergence, Complexity and Computation

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“…That have resulted to networks with greater resemblance to the man-made rail network. Adamatzky (2013) has studied the effects of network development by the plasmodium in terms of elevations on the surface of the laboratory experiment. The routes followed by immigrants from Mexico to the USA have been mim-icked by the analogous modeler with the usage of 3D Nylon terrains of USA.…”

Section: Related Workmentioning

confidence: 99%

Mimicking the Exploration of 3D Terrains by Physarum with Cellular Automata Models

Tsompanas

Sirakoulis

2015

07/20/2015-07/24/2015

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The plasmodium of Physarum polycephalum has been used to solve graphically represented puzzles and mimic the development of different types of networks in laboratory experiments. Thus, it can be characterized as a biological computing device. Although this non-silicon computer is mainly using input data solely coded as the distances between points of interest, some other environmental factors seem to have an effect on the behaviour of the plasmodium and, thus, the produced results. These factors can be elevations or illumination of the experimental surface. This paper presents a model based on Cellular Automata (CA) that imitates the results provided by the living substrate which is subjected to the effects of the elevations of the 3-dimensional (3D) experimental surface. Developing software based models is based on their robustness compared to the time-expensive real-life biological computing device. Some trivial experiments are presented to depict the agreement between the resultant networks developed by the simulated and the real plasmodium.

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“…adaptive and high efficient network in biological experiments [25], [26], [27], [28], [29], [30]. Moreover, Tero et al have captured the positive feedback mechanism of Physarum in foraging and have built a Physarum-inspired mathematical model (PMM) [31], [32], [33].…”

Section: Introductionmentioning

confidence: 99%

Solving NP-Hard Problems with <italic>Physarum</italic>-Based Ant Colony System

Liu

Gao

Zhang

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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NP-hard problems exist in many real world applications. Ant colony optimization (ACO) algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a Physarum-based pheromone matrix optimization strategy in ant colony system (ACS) for solving NP-hard problems such as traveling salesman problem (TSP) and 0/1 knapsack problem (0/1 KP). In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.

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Bio-Imitation of Mexican Migration Routes to the USA with Slime Mould on 3D Terrains

Cited by 21 publications

References 33 publications

Thirty Seven Things to Do with Live Slime Mould

Thirty Seven Things to Do with Live Slime Mould

Mimicking the Exploration of 3D Terrains by Physarum with Cellular Automata Models

Solving NP-Hard Problems with <italic>Physarum</italic>-Based Ant Colony System

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