New ideas in optimization
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
Real-parameter genetic algorithms for finding multiple optimal solutions in multi-modal optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A note on representations and variation operators
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Function optimization using cartesian genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Numerical optimization by multi-gene genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Evolutionary design of optical waveguide with multiple objectives
ACSC '12 Proceedings of the Thirty-fifth Australasian Computer Science Conference - Volume 122
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This paper describes a new approach to optimization that uses a novel representation for the parameters to be optimized. By using genetic programming, the method evolves a population of functions. The purpose of such functions is to transform initial random values of the parameters into better ones. The representation is, in principle, independent of the size of the problem being addressed. Promising results are reported, comparing the new method with differential evolution, particle swarm optimization, and genetic algorithms, on a test suite of benchmark problems.