PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Proceedings of the European Conference on Genetic Programming
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
A new crossover technique for Cartesian genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Comparison of CGP and Age-Layered CGP Performance in Image Operator Evolution
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Efficiently evolving programs through the search for novelty
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A many threaded CUDA interpreter for genetic programming
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
A robust stochastic genetic algorithm (StGA) for global numerical optimization
IEEE Transactions on Evolutionary Computation
Redundancy and computational efficiency in Cartesian genetic programming
IEEE Transactions on Evolutionary Computation
Corrections to “A Robust Stochastic Genetic Algorithm (StGA) for Global Numerical Optimization”
IEEE Transactions on Evolutionary Computation
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Genetic programming algorithms seek to find interpretable and good solutions for problems which are difficult to solve analytically. For example, we plan to use this paradigm to develop a car accident severity prediction model for new occupant safety functions. This complex problem will suffer from the major disadvantage of genetic programming, which is its high demand for computational effort to find good solutions. A main reason for this demand is a low rate of convergence. In this paper, we introduce a new genetic operator called forking to accelerate the rate of convergence. Our idea is to interpret individuals dynamically as centers of local Gaussian distributions and allow a sampling process in these distributions when populations get too homogeneous. We demonstrate this operator by extending the Cartesian Genetic Programming algorithm and show that on our examples convergence is accelerated by over 50% on average. We finish this paper with giving hints about parameterization of the forking operator for other problems.