Genetic programming III: Darwinian invention and problem solving [Book Review]

By John R. Koza, F. H. Bennett, David Andre, M. A. Keane. 1999

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In Reinforcement Learning

Citations in the corpus

Evaluating Large Language Models Trained on Code (2021, background): “Such attributes have long been discussed by the genetic programming community (Koza et al., 1999), and we note the relevant properties to modern day synthesis techniques below: • Code and parameterized reuse • Automatic determination of program architecture • Wide range of programming constructs •…”
A Hazard Analysis Framework for Code Synthesis Large Language Models (2022, neutral): “Such coding practices have long been discussed by the genetic programming community [22], and we note the relevant properties to modern day synthesis techniques below: • Code and parameterized reuse: Model has the ability to automatically organize useful groups of steps so that they can be reused.”
Automatic programming: The open issue? (2019, background): “Issues to consider include using various kinds of modularity, and multiple and possibly complex data types and control structures, and the potential to generate new instances of modularity, data and control structures, and to modify these on the fly [34, 38].”
Genetic Programming: An Introduction (1997, background): “Koza has been a prolific producer of documentation, including books (Koza, 1992; Koza et al., 1999) and videotapes related to genetic programming, and it is one of the fastest-growing and most fascinating areas of evolutionary computation.”
Principles in the Evolutionary Design of Digital Circuits—Part I (2004, methodology): “Koza et al. 1996b, 1999 have pioneered the extrinsic evolution of analogue electronic circuits using the SPICE simulator and have automatically generated circuits which are competitive with those of human designers.”
Ten years of genetic fuzzy systems: current framework and new trends (2001, background): “Designing fuzzy rule-based systems with genetic programming Genetic programming (GP) is concerned with the automatic generation of computer programs [42].”
A Genetic Algorithm for the Weight Setting Problem in OSPF Routing (2002, methodology): “At each iteration, a new generation of approximations is created by the process of selection and reproduction [19].”
Evolving rule-based systems in two medical domains using genetic programming (2004, methodology): “According to [7], throughout the execution of the algorithm, the expression that obtains good performance in the training and also in the test set is selected.”
Gene Expression Programming: A New Adaptive Algorithm for Solving Problems (2001, methodology): “As a comparison, the GP technique used populations of 51,200 individuals and 1000 ICs for 51 generations [13], thus a total of 51,200 x 1000 x 51 = 2,611,200,000 fitness evaluations were made, whereas GEP only made 30 x 25 x 50 = 37,500 fitness evaluations.”
Global Optimization Algorithms -- Theory and Application (2009, background): “Koza [608]: Genetic Programming, On the Programming of Computers by Means of Natural Selection Poli, Langdon, and McPhee [739]: A Field Guide to Genetic Programming Koza [740]: Genetic Programming II: Automatic Discovery of Reusable Programs: Automatic Discovery of Reusable Programs Koza, Bennett II”
Post-compiler software optimization for reducing energy (2014, methodology): “Evolutionary algorithms such as GOA are well-suited for exploring dif-ﬁcult search spaces [29].”