Search Results for "genetic algorithm engine"

GenDasm is a Generic Code-Seeking Diassembler engine. It allows you to enter known starting vectors for a given code image for the micro. It will disassemble the code and follow through branches to assist in the separation of code and data. Its included Fuzzy Function Analyzer companion uses a DNA Sequence Alignment Algorithm to locate similar code in multiple binaries to facilitate reverse-engineering and/or code recovery.

one C/C++ file & header ==> 8 functions: - beep - tray notify popup - message & question - input & password - save file - open file(s) - select folder - color picker complements OpenGL Vulkan GLFW GLUT GLUI VTK SFML TGUI SDL Ogre Unity3d ION OpenCV CeGui MathGL GLM CPW GLOW Open3D ImGui MyGui GLT NGL STB Nuklear Fenster MicroUi & head-less programs NO INIT NO MAIN LOOP NO LINKING NO INCLUDE win (XP to 11) ASCII MBCS UTF-8 UTF-16 (wchar_t) - native dialogs osx/unix...

Troll enables creation and visualisation of complex environments. It is based on Ogre3D (graphics engine), Bullet (physics engine), PhysX (physics engine), OpenAL (3D sound and music), OpenCV (image analysis and camera model) and Boost (threading and python binding). It uses Python for scripting, as well as built in simple scripting language. Some features: - built in multiplayer - incorporating data from different sensors (IMU, AHRS, Razer Hydra) - image server/client - built in support for motion capture data analysis - path finding using data from physics engine (backwave propagation algorithm) - built in XML parser - graphical world editor - plugins system (C++)

Classical genetic algorithm suffers heavy pressure of fitness evaluation for time-consuming optimization problems. To address this problem, we present an efficient genetic algorithm by the combination with clustering methods. The high efficiency of the proposed method results from the fitness estimation and the schema discovery of partial individuals in current population and.

A genetic algorithm framework to allow the evolution of synapse weights and topologies of artificial neural networks.

In silico evolution of biochemical networks using a genetic algorithm combined with a rule-based biochemical network model.