Artificial Intelligence  Machine Learning Problem Solving Environment for Complex Design in Science and Engineering
INTRODUCTION Machine learning, a cornerstone of artificial and computational intelligence defined loosely as a means to build approximations from data, has played a key role in reducing the impact of the aforementioned
challenge. The existing methods in the literature have been successfully applied in a host of practical domains spanning natural language processing, computer vision, recommender systems, etc. In complex design, machine learning is predominantly used for approximating the expensive physicsbased simulations or multiagentbased simulations using supervised regression models, or more commonly, surrogate models. Despite the promising outcomes achieved thus far, it is deemed that the full capabilities of machine learning are yet to be fully unveiled and exploited in this domain.


Approximation of Computational Expensive Analysis or Simulation Models



Aerodynamic Airfoil Wing Design 
Discovery of Isomers in H2O(n) Using 1st Principal Methods 



One of the wellknown strength of stochastic optimization is also in the ability to partition the population of individuals among multiple computing nodes. Doing so allows sublinear speedup in computation and even superlinear speedup if possible algorithmic speedup is also considered. When applied to small scale dedicated and homogeneous computing nodes, this seems to be a very formidable solution. In reallife situation, there are many cases where heterogeneity exists, e.g. in a Grid/Cloud computing environment, which emphasizes on the seamless sharing of computing resources across laboratories and even geographical boundaries, heterogeneity of the resources in the sharing pool is inevitable. In addition to that, function evaluation time can vary in many cases, for instance, in the case where the objective function is a variablefidelity function. In such situation a conventional parallelization without taking into account the heterogeneity of computing resources, might lead the EA to be ineffective. Hence, a suitable parallel optimization framework that fit in a heterogeneous computing environment while maintaining (or improving) the good search property of stochastic optimization is developed. 

Parallel Hierarchical Genetic Algorithm on The Grid/Cloud


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