Genetic programming as a means for programming computers by natural selection

By John R. Koza. 1994

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Quoc V. Le
2. Yoshua Bengio
3. Yi Yang
4. Xuanyi Dong
5. Percy Liang
6. Mohit Bansal
7. Trevor Darrell
8. Zhe Gan

Citations in the corpus

• What do we need to build explainable AI systems for the medical domain? (2017, background): “Very useful for building explainable systems in the medical domain is generally ge-netic programming (Koza, 1994; Pena-Reyes and Sipper, 1999; Tsakonas et al., 2004), and specifically evolutionary algorithms (Wang and Palade, 2011; Holzinger et al., 2014, 2016).”
• Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “2 Genetic Programming (GP) Genetic programming (GP) differs from genetic algorithms mainly in the form of the input and output values the algorithm needs and produces [41].”
• Interpretable scientific discovery with symbolic regression: a review (2022, unknown): “Within the same category, there exist several benchmarks [1, 14, 15, 16, 17, 18, 19] consisting of real-valued symbolic functions.”
• Program Synthesis (2017, background): “There has also been a lot of pioneering work on using genetic programming approaches to automatically evolve programs that are consistent with a specification [73].”
• Mathematical discoveries from program search with large language models (2023, methodology): “To surpass the ‘nominal’ capabilities of LLMs, recent studies 3 have combined them with evolutionary algorithms 14,15 , leading to important improvements on diverse synthetic problems 16 , searching for neural network architectures 17–19 and solving puzzles 20 .”
• BUSTLE: Bottom-up program-Synthesis Through Learning-guided Exploration (2020, background): “, 2013), genetic programming (Koza, 1994), reducing the synthesis problem to logical satisfiability (Solar-Lezama et al.”
• Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 (2020, methodology): “Syntax trees are developed using the stochastic genetic programming technique from the field of evolutionary algorithms [74].”
• Genetic algorithms: theory, genetic operators, solutions, and applications (2023, unknown): “Numerous EAs are developed, such as Genetic Algorithms (GAs) [6–8], Genetic Programming (GP) [9], Evolutionary Programming (EP) [10], Deferential Evolution [11] and Evolution Strategy (ES) [12].”
• AlphaEvolve: A coding agent for scientific and algorithmic discovery (2025, unknown): “AlphaEvolve extends a long tradition of research on evolutionary or genetic programming [54], where one repeatedly uses a set of mutation and crossover operators to evolve a pool of programs [5, 51].”
• Symbolic Discovery of Optimization Algorithms (2023, methodology): “There is also a long history of using evolutionary methods to search for programs, i.e., genetic programming [11, 42, 49].”
• Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020, background): “Similarly, Genetic programming (GP) was first proposed by Cramer [36] and further enhanced by Koza [37,38].”