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2015 | 16 | 53-72
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Find Optimal Solution for Eggcrate Function Based on Objective Function

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In modern compilers and optimizers, the set of possible optimizations is usually very large. In general, for a given application and workload, optimization options do not accrue toward ultimate performance. To avoid selection complexity, users tend to use standard combination options, in general, however, these standard options are not the optimal set for a specific application executing its representative workload. Genetic algorithm developed by Goldberg was inspired by Darwin's theory of evolution which states that the survival of an organism is affected by rule "the strongest species that survives". Darwin also stated that the survival of an organism can be maintained through the process of reproduction, crossover and mutation. Darwin's concept of evolution is then adapted to computational algorithm to find solution to a problem called objective function in natural fashion. In this work test the function known as the “Eggcrate Function”; is described mathematically as: F(X) = X12 + X22 + 25(Sin2X1 + Sin2X2) In this problem, there are two design variables with lower and upper limits of [-2π , 2π]. We use genetic algorithm to find optimal solution for solving this problem, Basic philosophy of genetic algorithm and its flowchart are described. Step by step numerical computation of genetic algorithm for solving the eggcrate function will be briefly explained. The results shows that the eggcrate function has a known global minimum at [0, 0] with an optimal function value of zero.

Physical description
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