Fly algorithm

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Evolutionary algorithm
Chromosome Fitness function Genetic operator Crossover Mutation Selection Population model
Genetic algorithm (GA)
Chromosome Clonal selection algorithm Fly algorithm Genetic fuzzy systems Genetic memory Schema Promoter based GA
Genetic programming (GP)
Cartesian GP Linear GP Gene expression programming Grammatical evolution Multi expression programming
Differential evolution
Evolution strategy
Evolutionary programming
Related topics
Cellular EA Cultural algorithm Effective fitness Evolutionary computation Gaussian adaptation Grammar induction Evolutionary multimodal optimization Memetic algorithm Neuroevolution
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The Fly Algorithm is a computational method within the field of evolutionary algorithms, designed for direct exploration of 3D spaces in applications such as computer stereo vision, robotics, and medical imaging. Unlike traditional image-based stereovision, which relies on matching features to construct 3D information, the Fly Algorithm operates by generating a 3D representation directly from random points, termed "flies." Each fly is a coordinate in 3D space, evaluated for its accuracy by comparing its projections in a scene. By iteratively refining the positions of flies based on fitness criteria, the algorithm can construct an optimized spatial representation. The Fly Algorithm has expanded into various fields, including applications in digital art, where it is used to generate complex visual patterns.