Search Results for "evolution" - Page 4

The Athlete is a virtual robot living in the ODE (Open Dynamics Engine)world. An evolution algorithm is implemented based on the DR-EA-M to optimize its performances. This involves a Genetic algorithm, network of neurons and morphology. All is in java.

Gnubert will be a system to use the power of many computers across the internet to solve problems using evolutionary techniques. Any user may define a problem and the system will attempt to solve it.

The genesis-ai project is an attempt to develop complex neural network behavior by means of evolutionary techniques.

An OpenGL simulation of artificial life and artificial evolution using neural networks and genetic algorithms based on Larry Yaeger's PolyWorld.

Domigene is a genetic algorithm API attempting to incorporate dominant and recessive genes. Supports modular evolution types, genetic programs, definable instruction sets, variation in population, chromosomes, mortality, mutation, fitness functions.

Biobox is an alife program based (loosely) on soda constructor at www.sodaplay.com. The main differences being that it is 3d, and involves evolution (natural selection will provided by the user initially).

...These models are part of the broader Mistral Small family, which is designed to deliver strong performance across a wide range of everyday AI tasks while maintaining relatively low latency and efficient deployment requirements. The collection reflects an evolution toward hybrid mixture-of-experts architectures that dynamically activate subsets of parameters during inference, allowing large models to remain computationally efficient. Mistral Small 4 models are built to handle tasks such as conversational AI, software development assistance, and reasoning-heavy problem solving, making them versatile tools for both developers and enterprise applications.

MiniMax-M2.7 is a large-scale open-weight language model designed for advanced agent-based workflows, professional software engineering, and complex productivity tasks. With 229B parameters, it introduces a self-evolution framework in which the model actively improves its own capabilities by updating memory, generating skills, and iterating through reinforcement learning experiments. This process enables it to autonomously refine systems, achieving measurable performance gains such as a 30% improvement in programming scaffolds. M2.7 excels in real-world engineering scenarios, including debugging, log analysis, system monitoring, and root cause investigation, demonstrating strong system-level reasoning comparable to SRE workflows. ...

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