Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
A Re-examination Of The Cart Centering Problem Using The Chorus System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Is The Perfect The Enemy Of The Good?
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
An Investigation into the Use of Different Search Strategies with Grammatical Evolution
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Policy Evolution with Grammatical Evolution
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
On the avoidance of fruitless wraps in grammatical evolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
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One of the key characteristics of Evolutionary Algorithms is the manner in which solutions are evolved from a primordial soup. The way this soup, or initial generation, is created can have major implications for the eventual quality of the search, as, if there is not enough diversity, the population may become stuck on a local optimum. This paper reports an initial investigation using a position independent evolutionary algorithm, Chorus, where the usual random initialisation has been compared to an approach modelled on the GP ramped half and half method. Three standard benchmark problems have been chosen from the GP literature for this study. It is shown that the new initialisation method, termed sensible initialisation maintains populations with higher average fitness especially earlier on in evolution than with random initialisation. Only one of the benchmarks fails to show an improvement in a probability of success measure, and we demonstrate that this is more likely a symptom of issues with that benchmark than with the idea of sensible initialisation. Performance seems to be unaffected by the different derivation tree depths used, and having a wider pool of individuals, regardless of their average size, seems enough to improve the performance of the system.