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 Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
Large scale evolutionary optimization using cooperative coevolution
Information Sciences: an International Journal
Routine high-return human-competitive automated problem-solving by means of genetic programming
Information Sciences: an International Journal
Multiclass Object Recognition Based on Texture Linear Genetic Programming
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Parallel linear genetic programming
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Linear genetic programming for multi-class object classification
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Parallel linear genetic programming for multi-class classification
Genetic Programming and Evolvable Machines
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Parallel Linear Genetic Programming (PLGP) is an architecture that addresses instruction dependencies in Linear Genetic Programming (LGP). The Co-operative Coevolution (CC) methodology has previously been applied to PLGP but implementations have not been able to improve performance over vanilla PLGP. In this paper we present Hill Climbing Parallel Linear Genetic Programming (HC-PLGP) which uses a local search to discover effective combinations (blueprints) of partial solutions that are evolved in subpopulations. By introducing a new caching technique we can efficiently search over the subpopulations, and our improved fitness function combined with normalisation and blueprint elitism address some of the weaknesses of the previous approaches. Hill Climbing Parallel Linear Genetic Programming (HC-PLGP) is compared to three PLGP architectures over six datasets, and significantly outperforms them on two datasets, is comparable on three, and is slightly worse on one dataset.