The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
The Darwinian Genetic Code: An Adaptation for Adapting?
Genetic Programming and Evolvable Machines
Tracking the Red Queen: Measurements of Adaptive Progress in Co-Evolutionary Simulations
Proceedings of the Third European Conference on Advances in Artificial Life
Genotype-Phenotype-Mapping and Neutral Variation - A Case Study in Genetic Programming
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
An Adaptive Mapping for Developmental Genetic Programming
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
On identifying global optima in cooperative coevolution
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Ideal Evaluation from Coevolution
Evolutionary Computation
Robustness in cooperative coevolution
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Genetic programming using genotype-phenotype mapping from linear genomes into linear phenotypes
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
An adverse interaction between crossover and restricted tree depth in genetic programming
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
AntNet: distributed stigmergetic control for communications networks
Journal of Artificial Intelligence Research
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
mGGA: the meta-grammar genetic algorithm
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Biasing Coevolutionary Search for Optimal Multiagent Behaviors
IEEE Transactions on Evolutionary Computation
Learning recursive programs with cooperative coevolution of genetic code mapping and genotype
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming
EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
Soft memory for stock market analysis using linear and developmental genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Rethinking multilevel selection in genetic programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Large network analysis for fisheries management using coevolutionary genetic algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An investigation of local patterns for estimation of distribution genetic programming
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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Developmental Genetic Programming (DGP) algorithms have explicitly required the search space for a problem to be divided into genotypes and corresponding phenotypes. The two search spaces are often connected with a genotype-phenotype mapping (GPM) intended to model the biological genetic code, where current implementations of this concept involve evolution of the mappings along with evolution of the genotype solutions. This work presents the Probabilistic Adaptive Mapping DGP (PAM DGP), a new developmental implementation that involves research contributions in the areas of GPMs and coevolution. The algorithm component of PAM DGP is demonstrated to overcome coevolutionary performance problems that are identified and empirically benchmarked against the latest competing algorithm that adapts similar GPMs. An adaptive redundant mapping encoding is then incorporated into PAM DGP for further performance enhancement. PAM DGP with two mapping types are compared to the competing Adaptive Mapping algorithm and Traditional GP in two medical classification domains, where PAM DGP with redundant encodings is found to provide superior fitness performance over the other algorithms through it's ability to explicitly decrease the size of the function set during evolution.