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In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions. Evolution of the population then takes place after the repeated application of the above operators.
Swarm behaviour, or swarming, is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic.
Virulence is a pathogen's or microorganism's ability to cause damage to a host.
In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. Artificial ants stand for multi-agent methods inspired by the behavior of real ants. The pheromone-based communication of biological ants is often the predominant paradigm used. Combinations of artificial ants and local search algorithms have become a method of choice for numerous optimization tasks involving some sort of graph, e.g., vehicle routing and internet routing.
Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, tune, or select a heuristic that may provide a sufficiently good solution to an optimization problem or a machine learning problem, especially with incomplete or imperfect information or limited computation capacity. Metaheuristics sample a subset of solutions which is otherwise too large to be completely enumerated or otherwise explored. Metaheuristics may make relatively few assumptions about the optimization problem being solved and so may be usable for a variety of problems.
Proteus mirabilis is a Gram-negative, facultatively anaerobic, rod-shaped bacterium. It shows swarming motility and urease activity. P. mirabilis causes 90% of all Proteus infections in humans. It is widely distributed in soil and water. Proteus mirabilis can migrate across the surface of solid media or devices using a type of cooperative group motility called swarming. Proteus mirabilis is most frequently associated with infections of the urinary tract, especially in complicated or catheter-associated urinary tract infections.
Waggle dance is a term used in beekeeping and ethology for a particular figure-eight dance of the honey bee. By performing this dance, successful foragers can share information about the direction and distance to patches of flowers yielding nectar and pollen, to water sources, or to new nest-site locations with other members of the colony.
Anaeroplasmatales is an order of mollicute bacteria which are generally found in the rumens of cattle and sheep. The only family in the order is the family Anaeroplasmataceae.
Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods that guide the search for the optimum by building and sampling explicit probabilistic models of promising candidate solutions. Optimization is viewed as a series of incremental updates of a probabilistic model, starting with the model encoding an uninformative prior over admissible solutions and ending with the model that generates only the global optima.
Gut microbiota, gut microbiome, or gut flora are the microorganisms, including bacteria, archaea, fungi, and viruses, that live in the digestive tracts of animals. The gastrointestinal metagenome is the aggregate of all the genomes of the gut microbiota. The gut is the main location of the human microbiome. The gut microbiota has broad impacts, including effects on colonization, resistance to pathogens, maintaining the intestinal epithelium, metabolizing dietary and pharmaceutical compounds, controlling immune function, and even behavior through the gut–brain axis.
Microbial intelligence is the intelligence shown by microorganisms. The concept encompasses complex adaptive behavior shown by single cells, and altruistic or cooperative behavior in populations of like or unlike cells mediated by chemical signalling that induces physiological or behavioral changes in cells and influences colony structures.
In microbiology, colony-forming unit is a unit which estimates the number of microbial cells in a sample that are viable, able to multiply via binary fission under the controlled conditions. Counting with colony-forming units requires culturing the microbes and counts only viable cells, in contrast with microscopic examination which counts all cells, living or dead. The visual appearance of a colony in a cell culture requires significant growth, and when counting colonies, it is uncertain if the colony arose from one cell or a group of cells. Expressing results as colony-forming units reflects this uncertainty.
In computer science and operations research, the bees algorithm is a population-based search algorithm which was developed by Pham, Ghanbarzadeh et al. in 2005. It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the bees algorithm is that some measure of distance between the solutions is defined. The effectiveness and specific abilities of the bees algorithm have been proven in a number of studies.
A prokaryote is a single-cell organism whose cell lacks a nucleus and other membrane-bound organelles. The word prokaryote comes from the Ancient Greek πρό 'before' and κάρυον 'nut, kernel'. In the two-empire system arising from the work of Édouard Chatton, prokaryotes were classified within the empire Prokaryota. But in the three-domain system, based upon molecular analysis, prokaryotes are divided into two domains: Bacteria and Archaea. Organisms with nuclei are placed in a third domain, Eukaryota.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987 Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
Virtual colony count (VCC) is a kinetic, 96-well microbiological assay originally developed to measure the activity of defensins. It has since been applied to other antimicrobial peptides including LL-37. It utilizes a method of enumerating bacteria called quantitative growth kinetics, which compares the time taken for a bacterial batch culture to reach a threshold optical density with that of a series of calibration curves. The name VCC has also been used to describe the application of quantitative growth kinetics to enumerate bacteria in cell culture infection models. Antimicrobial susceptibility testing (AST) can be done on 96-well plates by diluting the antimicrobial agent at varying concentrations in broth inoculated with bacteria and measuring the minimum inhibitory concentration that results in no growth. However, these methods cannot be used to study some membrane-active antimicrobial peptides, which are inhibited by the broth itself. The virtual colony count procedure takes advantage of this fact by first exposing bacterial cells to the active antimicrobial agent in a low-salt buffer for two hours, then simultaneously inhibiting antimicrobial activity and inducing exponential growth by adding broth. The growth kinetics of surviving cells can then be monitored using a temperature-controlled plate reader. The time taken for each growth curve to reach a threshold change in optical density is then converted into virtual survival values, which serve as a measure of antimicrobial activity.
Steinernema carpocapsae is an entomopathogenic nematode and a member of the family Steinernematidae. It is a parasitic roundworm that has evolved an insect-killing symbiosis with bacteria, and kills its hosts within a few days of infection. This parasite releases its bacterial symbiont along with a variety of proteins into the host after infection, and together the bacteria and nematode overcome host immunity and kill the host quickly. As a consequence, S. carpocapsae has been widely adapted for use as a biological control agent in agriculture and pest control. S. carpocapsae is considered a generalist parasite and has been effectively used to control a variety of insects including: Webworms, cutworms, armyworms, girdlers, some weevils, and wood-borers. This species is an example of an "ambush" forager, standing on its tail in an upright position near the soil surface and attaching to passing hosts, even capable of jumping. As an ambush forager, S. carpocapsae is thought to be especially effective when applied against highly mobile surface-adapted insects. S. carpocapsae can sense carbon dioxide production, making the spiracles a key portal of entry into its insect hosts. It is most effective at temperatures ranging from 22–28 °C (72–82 °F).
This is a chronological table of metaheuristic algorithms that only contains fundamental algorithms. Hybrid algorithms and multi-objective algorithms are not listed in the table below.
References
- ↑ Niu, Ben; Wang, Hong (2012). "Bacterial Colony Optimization" (PDF). Discrete Dynamics in Nature and Society. 2012: 1–28. doi: 10.1155/2012/698057 .
- ↑ Pang, Shinsiong; Chen, Mu-Chen (June 1, 2023). "Optimize railway crew scheduling by using modified bacterial foraging algorithm" . Computers & Industrial Engineering. 180: 109218. doi:10.1016/j.cie.2023.109218. ISSN 0360-8352. S2CID 257990456.
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