Genetic programming (another name for evolutionary systems) creates generations of computer programs "using the principles of Darwinian natural selection and biologically inspired operations. The operations include reproduction, crossover (sexual recombination), mutation, and architecture-altering operations patterned after gene duplication and gene deletion in nature."
– Genetic Programming, Inc.
These combinations of real-time biological systems can blend knowledge, exploration, and exploitation to unify intelligence and solve problems more efficiently. These simple agents interact locally, within their environment, and new behaviors emerge from the group as a whole. In the world of evolutionary alogirthms one such inspired method is particle swarm optimization (PSO). It is a swarm intelligence based computational technique that can be used to find an approximate solution to a problem by iteratively trying to search candidate solutions (called particles) with regard to a given measure of quality around a global optimum. The movements of the particles are guided by their own best known position in the search-space as well as the entire swarm's best known position.
Did you know that there's a way to use the power of natural selection to solve programming challenges? It's when you want to find not just a valid solution but the solution that will give you the best results. For example, if you have a backpack that only fits a certain amount of stuff and you want to maximize the amount of stuff you can bring, then you could use a genetic algorithm to find the best solution. This is also known as *the knapsack problem*. The genetic algorithm is not the only way to solve this kind of problem, but it's an interesting one because it's modeled after real-world behavior.
Bio: Ahmed Gad received his B.Sc. degree with excellent with honors in information technology from the Faculty of Computers and Information (FCI), Menoufia University, Egypt, in July 2015. For being ranked first in his faculty, he was recommended to work as a teaching assistant in one of the Egyptian institutes in 2015 and then in 2016 to work as a teaching assistant and a researcher in his faculty. His current research interests include deep learning, machine learning, artificial intelligence, digital signal processing, and computer vision.
Selection of the optimal parameters for machine learning tasks is challenging. Some results may be bad not because the data is noisy or the used learning algorithm is weak, but due to the bad selection of the parameters values. This article gives a brief introduction about evolutionary algorithms (EAs) and describes genetic algorithm (GA) which is one of the simplest random-based EAs.
He started his talk with a broader philosophical statement: "Anything is possible." Referring to SpaceX and Tesla as the most cutting edge companies he has funded, he continued, "The future of technology that we couldn't imagine 15–20 years ago is obviously now possible. There is no doubt that the future of cars will be electric and autonomous.
An Atari-playing artificial intelligence created by researchers at the University of Freiburg in Germany has discovered a never-before-seen bug in the classic game Qbert. Using an inexplicable and seemingly random series of moves, the algorithm achieved an unprecedented high score in a matter of minutes.
Artificial intelligence is all the rage, but using swarm intelligence might be the best way to solve the world's biggest problems. Dr. Louis Rosenberg is the Founder & CEO of Unanimous AI, an artificial intelligence company that amplifies human intelligence by building "hive minds" modeled after biological swarms. Learn how swarm intelligence can combine the brainpower of humans and computers to solve humanity's biggest problems. Stream or download the podcast using the player below or find the episode everywhere podcasts are found, including iTunes, Stitcher, and Gretta.
This article provides an overview of evolutionary robotics research where evolution takes place in a population of robots in a continuous manner. Ficici et al. (1999) coined the phrase embodied evolution for evolutionary processes that are distributed over the robots in the population to allow them to adapt autonomously and continuously. As robotics technology becomes simultaneously more capable and economically viable, individual robots operated at large expense by teams of experts are increasingly supplemented by collectives of robots used cooperatively under minimal human supervision (Bellingham and Rajan, 2007), and embodied evolution can play a crucial role in enabling autonomous online adaptivity in such robot collectives.