The algorithmic beauty of plants
The algorithmic beauty of plants
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Genetic algorithms + data structures = evolution programs (2nd, extended ed.)
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
On Genetic Algorithms and Lindenmayer Systems
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Generative representations for evolutionary design automation
Generative representations for evolutionary design automation
Developmental evaluation in Genetic Programming: The TAG-based frame work
International Journal of Knowledge-based and Intelligent Engineering Systems - Genetic Programming An Emerging Engineering Tool
A model for intrinsic artificial development featuring structural feedback and emergent growth
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolution and analysis of a robot controller based on a gene regulatory network
ICES'10 Proceedings of the 9th international conference on Evolvable systems: from biology to hardware
Artificial Intelligence Review
| Hi-index | 0.00 |
Since typical evolutionary design systems encode only a single artifact with each individual, each time the objective changes a new set of individuals must be evolved. When this objective varies in a way that can be parameterized, a more general method is to use a representation in which a single individual encodes an entire class of artifacts. In addition to saving time by preventing the need for multiple evolutionary runs, the evolution of parameter-controlled designs can create families of artifacts with the same style and a reuse of parts between members of the family. In this paper an evolutionary design system is described which uses a generative representation to encode families of designs. Because a generative representation is an algorithmic encoding of a design, its input parameters are a way to control aspects of the design it generates. By evaluating individuals multiple times with different input parameters the evolutionary design system creates individuals in which the input parameter controls specific aspects of a design. This system is demonstrated on two design substrates: neural-networks which solve the 3/5/7-parity problem and three-dimensional tables of varying heights.