Some reflections on early history
A history of personal workstations
Automatic Recurrent and Feed-Forward ANN Rule and Expression Extraction with Genetic Programming
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Computational Intelligence: Concepts to Implementations
Computational Intelligence: Concepts to Implementations
Introduction to the special issue: Trends in evolutionary methods for program induction
Evolutionary Computation
Component Selection to Optimize Distance Function Learning in Complex Scientific Data Sets
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
An enhanced framework for microprocessor test-program generation
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Numerosity and the consolidation of episodic memory
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Exploiting auto-adaptive µGP for highly effective test programs generation
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
Open issues in genetic programming
Genetic Programming and Evolvable Machines
Quantum-inspired evolutionary algorithms: a survey and empirical study
Journal of Heuristics
ACAI '11 Proceedings of the International Conference on Advances in Computing and Artificial Intelligence
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Artificial Intelligence Review
Unearthing a Fossil from the History of Evolutionary Computation
Fundamenta Informaticae
A Historical Perspective on the Evolution of Executable Structures
Fundamenta Informaticae
An efficient distance metric for linear genetic programming
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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Machines would be more useful if they could learn to perform tasks for which they were not given precise methods. Difficulties that attend giving a machine this ability are discussed. It is proposed that the program of a stored-program computer be gradually improved by a learning procedure which tries many programs and chooses, from the instructions that may occupy a given location, the one most often associated with a successful result. An experimental test of this principle is described in detail. Preliminary results, which show limited success, are reported and interpreted. Further results and conclusions will appear in the second part of the paper.