Year: 2022

REINFORCED LEARNING. "A learning system is a system that improves its performance at some task with experience. A simple kind of learning (often likened to natural selection) is reinforcement learning. This utilises a reward function to reinforce good behaviour or good outputs (or punish bad outputs) when they occur." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

MACHINE LEARNING. "It is common to think of learning systems as sophisticated machines (or intelligent organisms) with goal-directed intentions designed for the purpose of producing smart behaviours—which would make their abilities irrelevant to understanding evolutionary processes." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

NOT MACHINE LEARNING. "But, in fact, all of the phenomena relevant to our evolutionary questions can be produced by intention-less algorithms with simple incremental improvement mechanisms." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

OPTIMIZING THE PROCESS NOT OUTPUT. "Note that in a learning system the object of this incremental improvement process is different from a conventional optimisation process. Whereas a simple processes of optimisation (or incremental improvement) is usually applied to a solution or output directly, a learning process optimises a model of good solutions or outputs or an indirect representation of solutions." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

PROCESS OPTIMIZATION. "Evolutionarily, this is like the difference between adapting the parameters of a phenotype directly (e.g. the traits of a phenotype) vs adapting the parameters of a developmental process that produces fit phenotypes (this is a distinction which is lost when we assume a one-to-one mapping between genotype and phenotype)." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

MODEL OPTIMIZATION. "The learning process optimises the fit of the model to the observations (or minimises the discrepancy between the model and the observations) by incrementally adjusting the parameters of the model." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

Check this group out. https://lnkd.in/dGbkFwC View in LinkedIn

GENERATING SOLUTION VARIATIONS. "The significance of this is that the model can then be used to recognise or generate new examples that have structural similarities with those that have been rewarded in the past but are not identical to them, and relatedly, examples that are far apart in ‘solution space’ can be placed next to each in ‘model space’." http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn

POSITIVE FEEDBACK HYPOTHESIS. "We investigate the hypothesis that the evolution of developmental, ecological and reproductive organisations are all subject to the same underlying organisational principle, a simple principle of positive feedback between the topology of an interaction network and the behaviours that the network structure governs." View in LinkedIn

POSITIVE FEEDBACK MODEL. "That is, a connection between two components or nodes in this network causes them to exhibit correlated behaviour, and when nodes have correlated behaviours this causes natural selection to create or strengthen the connection between them." Unpack slowly. http://link.springer.com/article/10.1007/s11692-015-9358-z View in LinkedIn