Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Genetic programming in C++: implementation issues
Advances in genetic programming
A parallel implementation of genetic programming that achieves super-linear performance
Information Sciences: an International Journal - special issue on parallel and distributed processing
Evolving complex robot behaviors
Information Sciences—Informatics and Computer Science: An International Journal
Genetic programming based pattern classification with feature space partitioning
Information Sciences: an International Journal
Robots playing soccer? RoboCup poses a new set of AI research challenges
IEEE Expert: Intelligent Systems and Their Applications
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
IEEE Transactions on Knowledge and Data Engineering
A generic ranking function discovery framework by genetic programming for information retrieval
Information Processing and Management: an International Journal
Journal of the American Society for Information Science and Technology
A comparison of bloat control methods for genetic programming
Evolutionary Computation
An integrated two-stage model for intelligent information routing
Decision Support Systems
Strategy creation, decomposition and distribution in particle navigation
Information Sciences: an International Journal
Thalassaemia classification by neural networks and genetic programming
Information Sciences: an International Journal
Genetic Programming-Based Discovery of Ranking Functions for Effective Web Search
Journal of Management Information Systems
Artificial Intelligence in Medicine
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Protein motif discovery with linear genetic programming
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Excluding fitness helps improve robustness of evolutionary algorithms
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Evolving pattern recognition systems
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
A genetic algorithm calibration method based on convergence due to genetic drift
Information Sciences: an International Journal
Information Sciences: an International Journal
Estimating software readiness using predictive models
Information Sciences: an International Journal
Dynamic population variation in genetic programming
Information Sciences: an International Journal
A fuzzy GARCH model applied to stock market scenario using a genetic algorithm
Expert Systems with Applications: An International Journal
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Information Sciences: an International Journal
Characterizing fault tolerance in genetic programming
Future Generation Computer Systems
Information Sciences: an International Journal
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A new population variation approach is proposed, whereby the size of the population is systematically varied during the execution of the genetic programming process with the aim of reducing the computational effort compared with standard genetic programming (SGP). Various schemes for altering population size under this proposal are investigated using a comprehensive range of standard problems to determine whether the nature of the ''population variation'', i.e. the way the population is varied during the search, has any significant impact on GP performance. The initial population size is varied in relation to the initial population size of the SGP such that the worst case computational effort is never greater than that of the SGP. It is subsequently shown that the proposed population variation schemes do have the capacity to provide solutions at a lower computational cost compared with the SGP.