scholarly journals Analysis of Lamarckian evolution in morphologically evolving robots

2017 ◽  
Author(s):  
Milan Jelisavcic ◽  
Rafael Kiesel ◽  
Kyrre Glette ◽  
Evert Haasdijk ◽  
A. E. Eiben
Keyword(s):  
Lamarckian Evolution

Belief Revision by Lamarckian Evolution

2001 ◽  
pp. 404-413 ◽  
Author(s):  
Evelina Lamma ◽  
Luís MonizPereira ◽  
Fabrizio Riguzzi
Keyword(s):  
Belief Revision ◽  
Lamarckian Evolution

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.

Belief revision via Lamarckian evolution

2003 ◽  
Vol 21 (3) ◽  
pp. 247-275 ◽  
Author(s):  
Evelina Lamma ◽  
Fabrizio Riguzzi ◽  
Luís Moniz Pereira
Keyword(s):  
Belief Revision ◽  
Lamarckian Evolution

scholarly journals How to Shift Bias: Lessons from the Baldwin Effect

1996 ◽  
Vol 4 (3) ◽  
pp. 271-295 ◽  
Author(s):  
Peter Turney
Keyword(s):  
Learning Algorithm ◽  
Inductive Learning ◽  
Learning Algorithms ◽  
Darwinian Evolution ◽  
Baldwin Effect ◽  
Lamarckian Evolution ◽  
Inheritance Of Acquired Characteristics ◽  
The Baldwin Effect ◽  
Fixed Bias ◽  
Shift Of Bias

An inductive learning algorithm takes a set of data as input and generates a hypothesis as output. A set of data is typically consistent with an infinite number of hypotheses; therefore, there must be factors other than the data that determine the output of the learning algorithm. In machine learning, these other factors are called the bias of the learner. Classical learning algorithms have a fixed bias, implicit in their design. Recently developed learning algorithms dynamically adjust their bias as they search for a hypothesis. Algorithms that shift bias in this manner are not as well understood as classical algorithms. In this paper, we show that the Baldwin effect has implications for the design and analysis of bias shifting algorithms. The Baldwin effect was proposed in 1896 to explain how phenomena that might appear to require Lamarckian evolution (inheritance of acquired characteristics) can arise from purely Darwinian evolution. Hinton and Nowlan presented a computational model of the Baldwin effect in 1987. We explore a variation on their model, which we constructed explicitly to illustrate the lessons that the Baldwin effect has for research in bias shifting algorithms. The main lesson is that it appears that a good strategy for shift of bias in a learning algorithm is to begin with a weak bias and gradually shift to a strong bias.

scholarly journals THE HISTORICAL/EVOLUTIONARY CAUSE AND POSSIBLE TREATMENT OF PANDEMIC COVID-19 (SARS-CoV-2, 2019CORONAVIRUS)

2021 ◽  
Author(s):  
Sorush Niknamian
Keyword(s):  
Older Adults ◽  
Natural Selection ◽  
Respiratory Tract ◽  
Genome Size ◽  
Rna Viruses ◽  
Rna Virus ◽  
Influenza Viruses ◽  
Recombinant Viruses ◽  
Lamarckian Evolution ◽  
Tract Infections

Background: A virus is a small infectious agent that replicates only inside the living cells of an organism. Viruses can infect all types of life forms, from animals and plants to microorganisms, including bacteria and archaea. In evolution, viruses are an important means of horizontal gene transfer, which increases genetic diversity in a way analogous to sexual reproduction. Influenza (Including (COVID-19), is an infectious disease caused by an influenza virus. Some viruses especially smallpox, throughout history, has killed between 300-500 million people in its 12,000year existence. As modern humans increased in numbers, new infectious diseases emerged, including SARS-CoV-2. We have two groups of virus, RNA and DNA viruses. The most brutal viruses are RNA ones like COVID-19 (Sars-CoV-2 [1] Introduction: Coronaviruses are a group of viruses that cause diseases in mammals and birds. In humans, coronaviruses cause respiratory tract infections that are typically mild, such as some cases of the common cold (among other possible causes, predominantly rhinoviruses), though rarer forms can be lethal, such as SARS, MERS, and COVID-19. Symptoms vary in other species: in chickens, they cause an upper respiratory tract disease, while in cows and pigs they cause diarrhea. Coronaviruses constitute the subfamily Orthocoronavirinae, The genome size, coronaviruses ranges from approximately 27 to 34 kilobases, the largest among known RNA viruses. Discussions and Results: We have researched from the first virus in the planet to the last mutated version which is SARS-COV-2. We have collected many informative data in tables and figures to reach the main cause of 2019Coronavirus and calculated the probability and estimated deaths in the current time. We have discussed about the possible treatment and prevention of the virus and did algebraic calculations on the epidemiology, the size and even the future of this pandemic. The only era which any virus had not been epidemic, were through world war 2, were the German scientists had found the way to fight any viral infections which is very important and can help scientists to reach the main treatment of the new 2019-Coronavirus. We have sorted the deadly and non-deadly coronaviruses and explained how this epidemic had begun through Evolutionary Medicine (EM). The result of the article is that 16% of the whole population in the world has been contaminated which is 1248000000 of 7.8 billion people world-wide. SARS-CoV-2 is an RNA Virus. its nucleic acid is 2 single-stranded RNA (ssRNA). The polarity of this virus is positive-sense ((+) ssRNA). Positivesense viral RNA is similar to mRNA and thus can be immediately translated by the host cell. Recombination in RNA viruses appears to be an adaptation for coping with genome damage. Recombination can occur infrequently between animal viruses of the same species but of divergent lineages. The resulting recombinant viruses may sometimes cause an outbreak of infection in humans. RNA viruses have very high mutation rates This is one reason why it is difficult to make effective vaccines to prevent diseases caused by RNA viruses. The resulting recombinant viruses causes an outbreak of infection in humans. Conclusion: In conclusion, the mutation of the SARSCoV and influenza viruses through Drift and Reassortment is the main cause of SARS-CoV-2 through natural selection, Lamarckian Evolution and coevolution which caused this RNA virus so powerful, unpredicted and different in the genome size and nations worldwide. The first Pandemic of Influenza was first detected in 1732 and this virus evolved through natural selection till 2019 which caused the worldwide pandemic of SARS-CoV-2. Based on many studies, inhalation of Ozone plus Sulfur Dioxide, increasing the amounts of L-Glutathione (Which is low in children and older adults and this is the main reason why older adults and children die from this disease.) plus Viral Phage Therapy (VPT) which we discussed fully in this article can be the possible prime treatment of SARS-CoV-2 infection. The seasonal temperature cannot be useful in controlling/reducing the pandemic of this virus since the natural selection, Lamarckian Evolution and high mutation of the virus helps its survival. No antiviral drugs will be useful against SARSCoV-2 because of high rate of mutation and primarily adaptation of the virus to the drugs and even the environmental Temperature.

Sign in / Sign up

Export Citation Format

Share Document