Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Theoretical Computer Science
Foundations of genetic programming
Foundations of genetic programming
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Theoretical results in genetic programming: the next ten years?
Genetic Programming and Evolvable Machines
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This work shows asymptotic convergence to global optima for a family of dynamically scaled genetic programming systems where the underlying population consists of a fixed number of creatures (individuals) each of arbitrary size. The genetic programming systems use common mutation and crossover operators as well as fitness-proportional selection. In addition, the mutation and crossover rates are annealed to zero in predefined fashion over the course of the algorithm, and power-law scaling is used for the (possibly population-dependent) initial fitness function with (unbounded) logarithmic growth in the exponent.We assume that a set of globally optimal creatures for the optimization problem instance exists. In addition, it is assumed that the ratio of the best fitness of globally optimal creatures vs the fitness of other creatures is greater or equal a constant ρ1 in any population they jointly reside in. We discuss how both conditions can usually be satisfied in application settings. Under the above conditions, a selected, traceable sequence of probability distributions over the possible states of the properly scaled genetic programming system converge in time towards the convex set of probability distributions over uniform populations that contain only globally optimal creatures.