Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Automatic Creation of Human-Competitive Programs and Controllers by Means of Genetic Programming
Genetic Programming and Evolvable Machines
Graph Based GP Applied to Dynamical Systems Modeling
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Morphological algorithm design for binary images using genetic programming
Genetic Programming and Evolvable Machines
A domain-independentwindow approach to multiclass object detection using genetic programming
EURASIP Journal on Applied Signal Processing
Texture segmentation by genetic programming
Evolutionary Computation
An improved representation for evolving programs
Genetic Programming and Evolvable Machines
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
Multiple interactive outputs in a single tree: an empirical investigation
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Use of infeasible individuals in probabilistic model building genetic network programming
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A new, node-focused model for genetic programming
EuroGP'12 Proceedings of the 15th European conference on Genetic Programming
Single node genetic programming on problems with side effects
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Genetic programming as strategy for learning image descriptor operators
Intelligent Data Analysis
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Most artificial intelligence systems today work on simple problems and artificial domains because they rely on the accurate sensing of the task world. Object recognition is a crucial part of the sensing challenge and machine learning stands in a position to catapult object recognition into real world domains. Given that, to date, machine learning has not delivered general object recognition, we propose a different point of attack: the learning architectures themselves. We have developed a method for directly learning and combining algorithms in a new way that imposes little burden on or bias from the humans involved. This learning architecture, PADO, and the new results it brings to the problem of natural image object recognition is the focus of this report.