Scaling, machine learning, and genetic neural nets
Advances in Applied Mathematics
Proceedings of the seventh international conference (1990) on Machine learning
Artificial evolution for computer graphics
Proceedings of the 18th annual conference on Computer graphics and interactive techniques
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The object instancing paradigm for linear fractal modeling
Proceedings of the conference on Graphics interface '92
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
Spacetime constraints revisited
SIGGRAPH '93 Proceedings of the 20th annual conference on Computer graphics and interactive techniques
SIGGRAPH '94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Dynamics Algorithm
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
Plants, fractals, and formal languages
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Hi-index | 0.00 |
This article describes a system for the evolution and coevolution of virtual creatures that compete in physically simulated three-dimensional worlds. Pairs of individuals enter one-on-one contests in which they contend to gain control of a common resource. The winners receive higher relative fitness scores allowing them to survive and reproduce. Realistic dynamics simulation including gravity, collisions, and friction, restricts the actions to physically plausible behaviors. The morphology of these creatures and the neural systems for controlling their muscle forces are both genetically determined, and the morphology and behavior can adapt to each other as they evolve simultaneously. The genotypes are structured as directed graphs of nodes and connections, and they can efficiently but flexibly describe instructions for the development of creatures' bodies and control systems with repeating or recursive components. When simulated evolutions are performed with populations of competing creatures, interesting and diverse strategies and counterstrategies emerge.