Prediction in Selectionist Evolutionary Theory
نویسندگان
چکیده
Selectionist evolutionary theory has often been faulted for not making novel predictions that are surprising, risky, and correct. I argue that it in fact exhibits the theoretical virtue of predictive capacity in addition to two other virtues: explanatory unification and model fitting. Two case studies show the predictive capacity of selectionist evolutionary theory: parallel evolutionary change in E. coli and the origin of eukaryotic cells through endosymbiosis.
منابع مشابه
Selectionist and Evolutionary Approaches to Brain Function: A Critical Appraisal
We consider approaches to brain dynamics and function that have been claimed to be Darwinian. These include Edelman's theory of neuronal group selection, Changeux's theory of synaptic selection and selective stabilization of pre-representations, Seung's Darwinian synapse, Loewenstein's synaptic melioration, Adam's selfish synapse, and Calvin's replicating activity patterns. Except for the last ...
متن کاملBergson's "Matter and Memory" and modern selectionist theories of memory.
Bergson's reflections (in Matter and Memory, 1896) on memory anticipated development of modern selectionist theories of memory. Selectionist models offer new and potentially useful approaches to a theory of remembering. On the model of natural selection, these selectionist theories require at least two processing components: a device which generates a range of memory representations and a selec...
متن کاملEstimation of LPC coefficients using Evolutionary Algorithms
The vast use of Linear Prediction Coefficients (LPC) in speech processing systems has intensified the importance of their accurate computation. This paper is concerned with computing LPC coefficients using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Dif-ferential Evolution (DE) and Particle Swarm Optimization with Differentially perturbed Velocity (PSO-DV...
متن کاملUnsupervised Segmentation of Markov Random Eld Modeled Textured Images Using Selectionist Relaxation Unsupervised Segmentation of Markov Random Eld Modeled Textured Images Using Selectionist Relaxation
Among the existing texture segmentation methods, those relying on Markov random elds have retained substantial interest and have proved to be very eecient in supervised mode. The use of Markov random elds in unsupervised mode is however hampered by the parameter estimation problem. The recent solutions proposed to overcome this diiculty rely on the assumptions that the shapes of the textured re...
متن کامل