Month: September 2022

INTRON PADDING. "This could only solve their problem when introns were introduced into the representation. When padding separates expressed components of the bit string, crossover has a better chance of transferring a substring intact. The situation is the same in genetic programs." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

EMERGENT INTRONS. "The advantage of genetic programming introns is that they, they emerge naturally from the dynamics of the algorithm rather than being designed into the representation. It is important then to not impede this emergent property as it may be crucial to the successful development of genetic programs." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

HITCHHIKING. "In genetic algorithms, a set of genes that do not benefit a genotype are said to hitchhike when they are propagated in the population by virtue of other genes in the genotype. Typically, hitchhiking is detrimental to the evolutionary process since the poorer genes that proliferate with the beneficial genes displace their competitors from the population." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

PROGRAMMING INTRONS. "The emergence of introns in genetic programing happen as a natural consequence of the dynamics. The superfluous components provide no benefit in terms of fitness to the program but “ride along” with the subtrees they surround." http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

AUTONOMOUS. "Because of the opportunistic nature of empirical credit assignment, the evolved genetic programs are often complex, unreadable and incorporate programming practices that are distinct from strategies used by humans." BINGO! http://scholar.google.es/scholar_url?url=http://citeseerx.ist.psu.edu/viewdoc/download%3Fdoi%3D10.1.1.15.5594%26rep%3Drep1%26type%3Dpdf&hl=en&sa=X&scisig=AAGBfm0J-z_xL5_kjwEhLP0aVOrhbAtTfA&nossl=1&oi=scholarr&ved=0ahUKEwi3wpKxxs_MAhXG2B4KHXRODt0QgAMICigBMAA View in LinkedIn

PERPETUAL EMERGENCE. "Substantial effort in the genetic programming community is dedicated to exploring avenues for designing solutions to hard problems. In an ideal situation the solution would adapt to unseen new problems in a satisfactory manner. In our view, this can be achieved to a great extent if mechanisms for allowing perpetual emergence and autonomous response to emerging properties, not requiring external intervention, are put in place." http://eprints.aston.ac.uk/20778/1/Emergence_in_genetic_programming.pdf View in LinkedIn

EVOLVING ROBOTS LEARN TO LIE. "In an experiment run at the Laboratory of Intelligent Systems in the Ecole Polytechnique Fédérale of Lausanne, Switzerland, robots that were designed to cooperate in searching out a beneficial resource and avoiding a poisonous one learned to lie to each other in an attempt to hoard the resource." https://lnkd.in/g_45Y2T View in LinkedIn

DESIGN FOR LYING TEST. "The experiment involved 1,000 robots divided into 10 different groups. Each robot had a sensor, a blue light, and its own 264-bit binary code "genome" that governed how it reacted to different stimuli. The first generation robots were programmed to turn the light on when they found the good resource, helping the other robots in the group find it." https://lnkd.in/g_45Y2T View in LinkedIn

DECEITFUL BEHAVIOR. “After 500 generations, 60 percent of the robots had evolved to keep their light off when they found the good resource, hogging it all for themselves. Even more telling, a third of the robots evolved to actually look for the liars by developing an aversion to the light; the exact opposite of their original programming!" http://www.popsci.com/scitech/article/2009-08/evolving-robots-learn-lie-hide-resources-each-other View in LinkedIn

"THE ORIGINS of the the TechnoCore can be traced back to the experiments of the Old Earth scientist Thomas S. Ray, who attempted to create self-evolving artificial life on a virtual computer." https://www.reddit.com/r/todayilearned/comments/3d3vct View in LinkedIn