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: an introduction: on the automatic evolution of computer programs and its applications
Towards identifying populations that increase the likelihood of success in genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Probing for limits to building block mixing with a tunably-difficult problem for genetic programming
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Population implosion in genetic programming
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Are multiple runs of genetic algorithms better than one?
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Population variation in genetic programming
Information Sciences: an International Journal
Genetic programming on graphics processing units
Genetic Programming and Evolvable Machines
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
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A common method for improving a genetic programming search on difficult problems is either multiplying the number of runs or increasing the population size. In this paper we propose a new search strategy which attempts to obtain a higher probability of success with smaller amounts of computational resources. We call this model Divide & Conquer since our algorithm initially partitions the search space in smaller regions that are explored independently of each other. Then, our algorithm collects the most competitive individuals found in each partition and exploits them in order to get a solution. We benchmarked our proposal on three problem domains widely used in the literature. Our results show a significant improvement of the likelihood of success while requiring less computational resources than the standard algorithm.