Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Alternatives in automatic function definition: a comparison of performance
Advances in genetic programming
A comparative analysis of genetic programming
Advances in genetic programming
Discovery of subroutines in genetic programming
Advances in genetic programming
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Artificial Intelligence
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Genetic programming: a paradigm for genetically breeding populations of computer programs to solve problems
An analysis of genetic programming
An analysis of genetic programming
Bayesian Methods for Efficient Genetic Programming
Genetic Programming and Evolvable Machines
Evolutionary induction of sparse neural trees
Evolutionary Computation
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This paper studies how well the combination of simulated annealing and ADFs solves genetic programming (GP) style program discovery problems. On a suite composed of the even-k-parity problems for k = 3, 4, 5, it analyses the performance of simulated annealing with ADFs as compared to not using ADFs. In contrast to GP results on this suite, when simulated annealing is run with ADFs, as problem size increases, the advantage to using them over a standard GP program representation is marginal. When the performance of simulated annealing is compared to GP with both algorithm using ADFs on the even-3-parity problem GP is advantageous, on the even-4-parity problem SA and GP are equal, and on the even-5-parity problem SA is advantageous.