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.)
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Creation of Neural Networks Based on Developmental and Evolutionary Principles
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
ICAL 2003 Proceedings of the eighth international conference on Artificial life
Evolving Neural Networks through Augmenting Topologies
Evolving Neural Networks through Augmenting Topologies
Shortcomings with using edge encodings to represent graph structures
Genetic Programming and Evolvable Machines
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
A novel generative encoding for exploiting neural network sensor and output geometry
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Acquiring evolvability through adaptive representations
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
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This paper focuses on TWEANN (Topology and Weight Evolving Artificial Neural Network) methods based on indirect developmental encodings. TWEANNs are Evolutionary Algorithms (EAs) which evolve both topology and parameters (weights) of neural networks. Indirect developmental encoding is an approach inspired by multi-cellular organisms' development from a single cell (zygote) known from Nature. The possible benefits of such encoding can be seen in Nature: for example, human genome consists of roughly 30 000 genes, which describe more than 20 billion neurons, each linked to as many as 10 000 others. In this work we examine properties of known tree-based indirect developmental encodings: Cellular Encoding and Edge Encoding. Well known Genetic Programming is usualy used to evolve tree structures. We have employed its successors: Gene Expression Programming (GEP) and Grammatical Evolution (GE) to optimize the trees. The combination of well designed developmental encoding and proper optimization method should bring compact genomes able to describe large-scale, modular neural networks. We have compared GE and GEP using a benchmark and found that GE was able to find solution about 7 times faster then GEP. On the other hand GEP solutions were more compact.