On the Baldwin Effect

1999 ◽  
Vol 5 (3) ◽  
pp. 241-246 ◽  
Author(s):  
Larry Bull
Keyword(s):  
Fitness Landscape ◽  
Higher Learning ◽  
Learning Rate ◽  
Adaptation Process ◽  
High Learning ◽  
Baldwin Effect ◽  
Nk Model ◽  
Learning Rates ◽  
Rate Of Learning ◽  
The Baldwin Effect

In this article the effects of altering the rate and amount of learning on the Baldwin effect are examined. Using a version of the abstract tunable NK model, it is shown that the adaptation process is sensitive to the rate of learning, particularly as the correlation of the underlying fitness landscape varies. Typically a high learning rate proves most beneficial as landscape correlation decreases. It is also shown that the amount of learning can have a significant effect on the adaptation process, where increased amounts of learning prove beneficial under higher learning rates on uncorrelated landscapes.

Adding Learning to the Cellular Development of Neural Networks: Evolution and the Baldwin Effect

1993 ◽  
Vol 1 (3) ◽  
pp. 213-233 ◽  
Author(s):  
Frédéric Gruau ◽  
Darrell Whitley
Keyword(s):  
Neural Networks ◽  
Boolean Functions ◽  
Development Process ◽  
Developmental Process ◽  
Fitness Landscape ◽  
Biological Cell ◽  
Baldwin Effect ◽  
Genetic Search ◽  
Lamarckian Evolution ◽  
The Baldwin Effect

A grammar tree is used to encode a cellular developmental process that can generate whole families of Boolean neural networks for computing parity and symmetry. The development process resembles biological cell division. A genetic algorithm is used to find a grammar tree that yields both architecture and weights specifying a particular neural network for solving specific Boolean functions. The current study particularly focuses on the addition of learning to the development process and the evolution of grammar trees. Three ways of adding learning to the development process are explored. Two of these exploit the Baldwin effect by changing the fitness landscape without using Lamarckian evolution. The third strategy is Lamarckian in nature. Results for these three modes of combining learning with genetic search are compared against genetic search without learning. Our results suggest that merely using learning to change the fitness landscape can be as effective as Lamarckian strategies at improving search.

Investigating the learning curve in endoscopic compared to microscopic myringotomy and ventilation tube insertion

2020 ◽  
Vol 134 (6) ◽  
pp. 497-500
Author(s):  
O Denton ◽  
A Daglish ◽  
L Smallman ◽  
S Fishpool
Keyword(s):  
Learning Curve ◽  
Learning Curves ◽  
Learning Rate ◽  
Middle Ear Surgery ◽  
Ventilation Tube ◽  
Ear Surgery ◽  
Steep Learning Curve ◽  
Learning Rates ◽  
Significant Difference ◽  
Rate Of Learning

AbstractObjectiveRate of learning is often cited as a deterrent in the use of endoscopic ear surgery. This study investigated the learning curves of novice surgeons performing simulated ear surgery using either an endoscope or a microscope.MethodsA prospective multi-site clinical research study was conducted. Seventy-two medical students were randomly allocated to the endoscope or microscope group, and performed 10 myringotomy and ventilation tube insertions. Trial times were used to produce learning curves. From these, slope (learning rate) and asymptote (optimal proficiency) were ascertained.ResultsThere was no significant difference between the learning curves (p = 0.41). The learning rate value was 68.62 for the microscope group and 78.71 for the endoscope group. The optimal proficiency (seconds) was 32.83 for the microscope group and 27.87 for the endoscope group.ConclusionThe absence of a significant difference shows that the learning rates of each technique are statistically indistinguishable. This suggests that surgeons are not justified when citing ‘steep learning curve’ in arguments against the use of endoscopes in middle-ear surgery.

scholarly journals The Influence of Learning on Evolution: A Mathematical Framework

2009 ◽  
Vol 15 (2) ◽  
pp. 227-245 ◽  
Author(s):  
Ingo Paenke ◽  
Tadeusz J. Kawecki ◽  
Bernhard Sendhoff
Keyword(s):  
Fitness Landscape ◽  
Simulation Models ◽  
Mathematical Framework ◽  
Gain Function ◽  
Baldwin Effect ◽  
Positive Gain ◽  
Proportional Change ◽  
Acceleration And Deceleration ◽  
The Baldwin Effect ◽  
Evolutionary Fitness

The Baldwin effect can be observed if phenotypic learning influences the evolutionary fitness of individuals, which can in turn accelerate or decelerate evolutionary change. Evidence for both learning-induced acceleration and deceleration can be found in the literature. Although the results for both outcomes were supported by specific mathematical or simulation models, no general predictions have been achieved so far. Here we propose a general framework to predict whether evolution benefits from learning or not. It is formulated in terms of the gain function, which quantifies the proportional change of fitness due to learning depending on the genotype value. With an inductive proof we show that a positive gain-function derivative implies that learning accelerates evolution, and a negative one implies deceleration under the condition that the population is distributed on a monotonic part of the fitness landscape. We show that the gain-function framework explains the results of several specific simulation models. We also use the gain-function framework to shed some light on the results of a recent biological experiment with fruit flies.

On the Evolution of Multicellularity and Eusociality

1999 ◽  
Vol 5 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Larry Bull
Keyword(s):  
Fitness Landscape ◽  
Baldwin Effect ◽  
Daughter Cells ◽  
Unicellular Organisms ◽  
Subsequent Effect ◽  
The Baldwin Effect

In this article versions of the abstract NKC model are used to examine the conditions under which two significant evolutionary phenomena—multicellularity and eusociality—are likely to occur and why. First, comparisons in evolutionary performance are made between simulations of unicellular organisms and very simple multicellular-like organisms, under varying conditions. The results show that such multicellularity without differentiation appears selectively neutral, but that differentiation to soma (nonreproductives) proves beneficial as the amount of epistasis in the fitness landscape increases. This is explained by considering mutations in the generation of daughter cells and their subsequent effect on the propagule's fitness. This is interpreted as a simple example of the Baldwin effect. Second, the correspondences between multicellularity and eusociality are highlighted, particularly that both contain individuals who do not reproduce. The same process is then used to explain the emergence of eusocial colonies.

The Dynamic Changes in Roles of Learning through the Baldwin Effect

2007 ◽  
Vol 13 (1) ◽  
pp. 31-43 ◽  
Author(s):  
Reiji Suzuki ◽  
Takaya Arita
Keyword(s):  
Phenotypic Plasticity ◽  
Fitness Landscape ◽  
Evolutionary Model ◽  
Hill Climbing ◽  
Complex Environments ◽  
Baldwin Effect ◽  
Epistatic Interactions ◽  
Genetic Robustness ◽  
The Baldwin Effect ◽  
Insight Into

The interaction between evolution and learning called the Baldwin effect is a two-step evolutionary scenario caused by the balances between benefit and cost of learning in general. However, little is known about the dynamic evolution of these balances in complex environments. Our purpose is to give a new insight into the benefit and cost of learning by focusing on the quantitative evolution of phenotypic plasticity under the assumption of epistatic interactions. For this purpose, we have constructed an evolutionary model of quantitative traits by using an extended version of Kauffman's NK fitness landscape. Phenotypic plasticity is introduced into our model; whether each phenotype is plastic or not is genetically defined, and plastic phenotypes can be adjusted by learning. The simulation results clearly show that drastic changes in roles of learning cause three-step evolution through the Baldwin effect and also cause the evolution of genetic robustness against mutations. We also conceptualize four different roles of learning by using a hill-climbing image of a population on a fitness landscape.

scholarly journals Analisis Laju Pembelajaran dalam Mengklasifikasi Data Wine Menggunakan Algoritma Backpropagation

2019 ◽  
Vol 3 (2) ◽  
pp. 422
Author(s):  
Jaya Tata Hardinata ◽  
Harly Okprana ◽  
Agus Perdana Windarto ◽  
Widodo Saputra
Keyword(s):  
Network Architecture ◽  
Secondary Data ◽  
Research Data ◽  
Learning Rate ◽  
Learning Rates ◽  
Long Time ◽  
Fast Network ◽  
Rate Of Learning ◽  
The Right

Backpropagation is an artificial neural network that has the architecture in conducting training and determining the right parameters to produce the correct output of similar but not the same input. One of the parameters that influences the determination of bacpropagation architecture is the rate of learning, where if the value of the learning rate is too high then the network architecture becomes unstable otherwise if the value of the learning rate is too low the network architecture converges and takes a long time in training network architecture. This research data is secondary data sourced from UCI Data Mechine Learning. The best network architecture in this study is 13-10-3, with different learning rates ranging from 0.01, 0.03, 0.06, 0.01, 0.13, 0.16, 0.2, 0.23, 0.026, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.9. From the 21 different learning rate values in the 13-10-3 network architecture, it is found that the level of learning rate is very important to get the right and fast network architecture. This can be seen in experiments with a learning rate of 0.65 can produce a better level of accuracy compared to a learning rate smaller than 0.65.

scholarly journals Catecholaminergic modulation of meta-learning

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Jennifer L Cook ◽  
Jennifer C Swart ◽  
Monja I Froböse ◽  
Andreea O Diaconescu ◽  
Dirk EM Geurts ◽  
...  
Keyword(s):  
Causal Link ◽  
Human Learning ◽  
Learning Processes ◽  
Higher Learning ◽  
Learning Rate ◽  
Parameter Learning ◽  
Learning Rates ◽  
Meta Learning ◽  
Learning Parameter ◽  
Value Learning

The remarkable expedience of human learning is thought to be underpinned by meta-learning, whereby slow accumulative learning processes are rapidly adjusted to the current learning environment. To date, the neurobiological implementation of meta-learning remains unclear. A burgeoning literature argues for an important role for the catecholamines dopamine and noradrenaline in meta-learning. Here, we tested the hypothesis that enhancing catecholamine function modulates the ability to optimise a meta-learning parameter (learning rate) as a function of environmental volatility. 102 participants completed a task which required learning in stable phases, where the probability of reinforcement was constant, and volatile phases, where probabilities changed every 10–30 trials. The catecholamine transporter blocker methylphenidate enhanced participants’ ability to adapt learning rate: Under methylphenidate, compared with placebo, participants exhibited higher learning rates in volatile relative to stable phases. Furthermore, this effect was significant only with respect to direct learning based on the participants’ own experience, there was no significant effect on inferred-value learning where stimulus values had to be inferred. These data demonstrate a causal link between catecholaminergic modulation and the adjustment of the meta-learning parameter learning rate.

scholarly journals Development and the Baldwin Effect

2004 ◽  
Vol 10 (1) ◽  
pp. 39-63 ◽  
Author(s):  
Keith L. Downing
Keyword(s):  
Strong Correlation ◽  
Behavioral Plasticity ◽  
Developmental Process ◽  
Fitness Landscape ◽  
Natural World ◽  
Baldwin Effect ◽  
Developmental Mechanism ◽  
Phenotype Space ◽  
The Baldwin Effect ◽  
Proper Balance

Baldwin's classic hypothesis states that behavioral plasticity can speed evolution by (a) smoothing the fitness landscape and (b) indirect genetic assimilation of acquired characteristics. This latter phase demands a strong correlation between genotype and phenotype space. But the natural world shows signs of this correlation at only a very coarse level, since the intervening developmental process greatly complicates the mapping from genetics to physiology and ethology. Hence, development appears to preclude a strong Baldwin effect. However, by adding a simple developmental mechanism to Hinton and Nowlan's classic model of the Baldwin effect, and by allowing evolution to determine the proper balance between direct and indirect mapping of genome to phenotype, this research reveals several different effects of development on the Baldwin effect, some promoting and others inhibiting. Perhaps the most interesting result is an evolved cooperation between direct blueprints and indirect developmental recipes in searching for unstructured and partially structured target patterns in large, needle-in-the-haystack fitness landscapes.

scholarly journals The Evolution of Sex through the Baldwin Effect

2017 ◽  
Vol 23 (4) ◽  
pp. 481-492 ◽  
Author(s):  
Larry Bull
Keyword(s):  
Fitness Landscapes ◽  
The Other ◽  
Evolution Of Sex ◽  
Baldwin Effect ◽  
Nk Model ◽  
Rudimentary Form ◽  
The Baldwin Effect

This article suggests that the fundamental haploid-diploid cycle of eukaryotic sex exploits a rudimentary form of the Baldwin effect. With this explanation for the basic cycle, the other associated phenomena can be explained as evolution tuning the amount and frequency of learning experienced by an organism. Using the well-known NK model of fitness landscapes, it is shown that varying landscape ruggedness varies the benefit of the haploid-diploid cycle, whether based upon endomitosis or syngamy. The utility of pre-meiotic doubling and recombination during the cycle are also shown to vary with landscape ruggedness. This view is suggested as underpinning, rather than contradicting, many existing explanations for sex.

scholarly journals Modeling Changes in Probabilistic Reinforcement Learning during Adolescence

2020 ◽  
Author(s):  
Liyu Xia ◽  
Sarah L Master ◽  
Maria K Eckstein ◽  
Beth Baribault ◽  
Ronald E Dahl ◽  
...  
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Brain Development ◽  
Developmental Trajectory ◽  
Higher Learning ◽  
Learning Rate ◽  
Large Sample Size ◽  
Probabilistic Uncertainty ◽  
Learning Rates ◽  
Probabilistic Reinforcement

AbstractIn the real world, many relationships between events are uncertain and probabilistic. Uncertainty is also likely to be a more common feature of daily experience for youth because they have less experience to draw from than adults. Some studies suggests probabilistic learning may be inefficient in youth compared to adults [1], while others suggest it may be more efficient in youth that are in mid adolescence [2, 3]. Here we used a probabilistic reinforcement learning task to test how youth age 8-17 (N = 187) and adults age 18-30 (N = 110) learn about stable probabilistic contingencies. Performance increased with age through early-twenties, then stabilized. Using hierarchical Bayesian methods to fit computational reinforcement learning models, we show that all participants’ performance was better explained by models in which negative outcomes had minimal to no impact on learning. The performance increase over age was driven by 1) an increase in learning rate (i.e. decrease in integration time horizon); 2) a decrease in noisy/exploratory choices. In mid-adolescence age 13-15, salivary testosterone and learning rate were positively related. We discuss our findings in the context of other studies and hypotheses about adolescent brain development.Author summaryAdolescence is a time of great uncertainty. It is also a critical time for brain development, learning, and decision making in social and educational domains. There are currently contradictory findings about learning in adolescence. We sought to better isolate how learning from stable probabilistic contingencies changes during adolescence with a task that previously showed interesting results in adolescents. We collected a relatively large sample size (297 participants) across a wide age range (8-30), to trace the adolescent developmental trajectory of learning under stable but uncertain conditions. We found that age in our sample was positively associated with higher learning rates and lower choice exploration. Within narrow age bins, we found that higher saliva testosterone levels were associated with higher learning rates in participants age 13-15 years. These findings can help us better isolate the trajectory of maturation of core learning and decision making processes during adolescence.

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