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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.
Fetal programming, also known as prenatal programming, is the theory that environmental cues experienced during fetal development play a seminal role in determining health trajectories across the lifespan. Three main forms of programming that occur due to changes in the maternal environment are:
Programming and Metaprogramming in the Human Biocomputer: Theory and Experiments is a 1968 book by John C. Lilly. In the book, "the doctor imagines the brain as a piece of computer technology." [1] More specifically, he uses "the analogy of brain being the hardware, the mind being the software and consciousness being beyond both." [2]
With the theory of virtual alphabets, David E. Goldberg showed in 1990 that by using a representation with real numbers, an EA that uses classical recombination operators (e.g. uniform or n-point crossover) cannot reach certain areas of the search space, in contrast to a coding with binary numbers. [24]
There are more examples of AGA variants: Successive zooming method is an early example of improving convergence. [26] In CAGA (clustering-based adaptive genetic algorithm), [27] through the use of clustering analysis to judge the optimization states of the population, the adjustment of pc and pm depends on these optimization states.
Systems biology can be considered from a number of different aspects. As a field of study, particularly, the study of the interactions between the components of biological systems, and how these interactions give rise to the function and behavior of that system (for example, the enzymes and metabolites in a metabolic pathway or the heart beats).
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 ...
Modelling biological systems is a significant task of systems biology and mathematical biology. [ a ] Computational systems biology [ b ] [ 1 ] aims to develop and use efficient algorithms , data structures , visualization and communication tools with the goal of computer modelling of biological systems.