Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Issues in evolutionary robotics
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Genetic programming in C++: implementation issues
Advances in genetic programming
A hierarchical classifier system implementing a motivationally autonomous animat
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
| Hi-index | 0.00 |
In this paper we discuss the polymorphic abilities of a new distributed representation for genetic programming, called Genetically Programmed Networks. These are inspired in a common structure in natural complex adaptive systems, where system functionality frequently emerges from the combined functionality of simple computational entities, densely interconnected for information exchange. A Genetically Programmed Network can be evolved into a distributed program, a rule based system or a neural network with simple adjustments to the evolutionary algorithm. The space of possible network topologies can also be easily controlled. This allows the fast exploration of various search spaces thus increasing the possibility of finding a (or a better) solution. Experimental results are presented to support our claims.