Background
Many key issues on environmental protection and sustainability programs trace back to optimization problems. At Green Cortex, we believe that the human brain generates the most powerful self-organization and self-optimization mechanisms in existence. Our award-winning team strives to build sophisticated mathematical and computing systems based on genetic algorithms and artificial neural networks to best simulate the workings of a human brain. By mimicking the functions of the human brain we achieve optimization in areas such as: energy grid set-up, power transmission, traffic flow, environmental hazard protection, etc.
Solution
Optimization Systems based on Genetic Algorithms
Illustrated here are the principle steps of genetic algorithms which are the underlying technology used to optimize the most critical parameter values during a production or transmission process. The original problem is first encoded into a gene-like data array and then the re-combination or survival of the fittest selection process is applied. The mechanism is very efficient and successful in searching and finding local and global optima in even the most complex optimization landscapes. Nature’s genius invention turns out to be a very robust and sophisticated optimization approach which we at Green Cortex aim to harness and apply to environmental problem solving.
Key Modules of the Optimizer Cortex that Allow for Full Parallelization of Simulations
The Optimizer Cortex is designed in such a way that its modules can run massive parallel instructions in different computers simultaneously to dramatically increase processing and simulation speed. The sequential or parallel algorithms can therefore allow us to solve highly complex sustainability optimization problems.
Many key issues on environmental protection and sustainability programs trace back to optimization problems. At Green Cortex, we believe that the human brain generates the most powerful self-organization and self-optimization mechanisms in existence. Our award-winning team strives to build sophisticated mathematical and computing systems based on genetic algorithms and artificial neural networks to best simulate the workings of a human brain. By mimicking the functions of the human brain we achieve optimization in areas such as: energy grid set-up, power transmission, traffic flow, environmental hazard protection, etc.
Solution
Optimization Systems based on Genetic Algorithms
Illustrated here are the principle steps of genetic algorithms which are the underlying technology used to optimize the most critical parameter values during a production or transmission process. The original problem is first encoded into a gene-like data array and then the re-combination or survival of the fittest selection process is applied. The mechanism is very efficient and successful in searching and finding local and global optima in even the most complex optimization landscapes. Nature’s genius invention turns out to be a very robust and sophisticated optimization approach which we at Green Cortex aim to harness and apply to environmental problem solving.
Key Modules of the Optimizer Cortex that Allow for Full Parallelization of Simulations
The Optimizer Cortex is designed in such a way that its modules can run massive parallel instructions in different computers simultaneously to dramatically increase processing and simulation speed. The sequential or parallel algorithms can therefore allow us to solve highly complex sustainability optimization problems.