Memetic Computing for Computationally Expensive Problems
INTRODUCTION Evolutionary Algorithms (EA)
as a family of computational models inspired by the natural process of
evolution, have been applied with a great degree of success to complex
design optimization problems. Potential solutions are encoded into a
simple chromosomelike data structure, and recombination and/or mutation operators are
repeatedly applied to a population of such potential solutions until a
certain termination condition is reached. Their popularity lies in their
ease of implementation and the ability to locate designs close to the
global optimum.


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 EA 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 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 evolutionary optimization is developed. 

Parallel Hierarchical Genetic Algorithm on The Grid


SOURCE CODE Download A Generic SurrogateAssisted Memetic Search package is provided here for free download. For enquiry relating to the software package, please drop me an email at asysong@ntu.edu.sg.


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