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Genetic programming (GP) is an evolutionary algorithm, an artificial intelligence technique mimicking natural evolution, which operates on a population of programs. It applies the genetic operators selection according to a predefined fitness measure , mutation and crossover .
As a general rule of thumb genetic algorithms might be useful in problem domains that have a complex fitness landscape as mixing, i.e., mutation in combination with crossover, is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in. Observe that commonly used crossover operators ...
Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual ...
EXACT is based on the perfect phylogeny model, and uses a very fast homotopy algorithm to evaluate the fitness of different trees, and then it brute forces the tree search using GPUs, or multiple CPUs, on the same or on different machines: Brute force search and homotopy algorithm: Jia B., Ray S., Safavi S., Bento J. EzEditor [18]
Ab Initio gene prediction is an intrinsic method based on gene content and signal detection. Because of the inherent expense and difficulty in obtaining extrinsic evidence for many genes, it is also necessary to resort to ab initio gene finding, in which the genomic DNA sequence alone is systematically searched for certain tell-tale signs of protein-coding genes.
Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging problems at least approximately. Selection has a dual purpose: on the one hand, it can choose individual genomes from a population for subsequent breeding (e.g., using the crossover operator ...
Genetic algorithms deliver methods to model biological systems and systems biology that are linked to the theory of dynamical systems, since they are used to predict the future states of the system. This is just a vivid (but perhaps misleading) way of drawing attention to the orderly, well-controlled and highly structured character of ...
Genetic algorithms are well suited to solving production scheduling problems, because unlike heuristic methods genetic algorithms operate on a population of solutions rather than a single solution. In production scheduling this population of solutions consists of many answers that may have different sometimes conflicting objectives.