Common machine learning algorithm implementations
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Updated
Jun 5, 2024 - C#
Common machine learning algorithm implementations
Exploring computer generating software using genetic evolution techniques
HeuristicLab - An environment for heuristic and evolutionary optimization
GPdotNET is an open source computer program for running tree based genetic programming.
A strongly-typed genetic programming implementation in C#
Implementation of a Genetic Algorithm in C#
The GeneticDFA project uses genetic programming to reverse engineer blackbox systems modeled in DFA form.
ABL-Unity3D is a GUI-based and efficient Genetic Programming (GP) and AI Planning framework designed for agent-based learning (ABL) research.
Using artificial intelligence and genetic algorithms to automatically write programs. Tutorial: http://www.primaryobjects.com/cms/article149
Simple Genetic Algorithm
C# implementation of the various algorithms based on Genetic Algorithm, Genetic Programming and Artificial Neural Networks.
Genetic Algorithm tests in C#. Simple string finder. Robot controller for maze solving. Scheduler with optimal timetable. Traveling salesman problem. Generic GA. Lottery prediction.
🧬 An implementation of optimized crossover for independent set problem
A lightweight solution allowing you to solve problems using genetic programming and genetic algorithms.
Gene Hackman is a framework for genetic programming. A novel feature is that it supports an ongoing tournament mode, where programs compete in real time rather than well-defined and distinct generations. This lends itself to certain problems well, such as those where programs can interact with each other. It also supports parallelizing the simul…
Genetic Programming Engine designed for .NET
ProcJam 2018 Entry. Simulate basic population genetics on fake creatures' DNA. Concepts of mutation, crossover, fitness distribution covered.
This genetic algorithm uses natural selection and mutation to generate a random 'being' which fulfill a determined goal.
Socio-Emotional Reward Design for Intrinsically-Motivated Reinforcement Learning Agents
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