A Highly Efficient Function Optimization with Genetic Programming

This paper describes a new approach for function optimization that uses a novel representation for the parameters to be optimized. By using genetic programming using, the new method evolves functions that transform initial random values for the parameters into optimal ones. Moreover, the new approach addresses the scalability problem by using a representation that, in principle, is independent of the size of the problem being addressed. Promising results are reported, comparing the new method with differential evolution and particle swarm optimization on a test suite of benchmark problems.

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