Indirectly Encoded Sodarace for Artificial Life
نویسندگان
چکیده
The aim of this paper is to introduce a lightweight twodimensional domain for evolving diverse and interesting artificial creatures. The hope is that this domain will fill a need for such an easily-accessible option for researchers who wish to focus more on the evolutionary dynamics of artificial life scenarios than on building simulators and creature encodings. The proposed domain is inspired by Sodarace, a construction set for two-dimensional creatures made of masses and springs. However, unlike the original Sodarace, the indirectly encoded Sodarace (IESoR) system introduced in this paper allows evolution to discover a wide range of complex and regular ambulating creature morphologies by encoding them with compositional pattern producing networks (CPPNs), which are an established indirect encoding originally introduced for encoding large-scale neural networks. The result, demonstrated through a technique called novelty search with local competition (which are combined through multiobjective search), is that IESoR can discover a wide breadth of interesting and functional creatures, suggesting its potential utility for future experiments in artificial life.
منابع مشابه
Do we spontaneously form stable trustworthiness impressions from facial appearance?
It is widely assumed among psychologists that people spontaneously form trustworthiness impressions of newly encountered people from their facial appearance. However, most existing studies directly or indirectly induced an impression formation goal, which means that the existing empirical support for spontaneous facial trustworthiness impressions remains insufficient. In particular, it remains ...
متن کاملEvolving Chart Pattern Sensitive Neural Network Based Forex Trading Agents
Though machine learning has been applied to the foreign exchange market for algorithmic trading for quiet some time now, and neural networks(NN) have been shown to yield positive results, in most modern approaches the NN systems are optimized through traditional methods like the backpropagation algorithm for example, and their input signals are price lists, and lists composed of other technical...
متن کاملUnderstanding the Regulation of Predatory and Anti-prey Behaviours for an Artificial Organism
An organism’s behaviour can be categorised as being either predatory or anti-prey. Predatory behaviours are behaviours that try to improve the life of an organism. Anti-prey behaviours are those that attempt to prevent death. Regulation between these two opposing behaviours is necessary to ensure survivability—and gene regulatory networks and metabolic networks are the mechanisms that provide t...
متن کاملIndirectly Encoding Neural Plasticity as a Pattern of Local Rules
Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This pap...
متن کاملR-HybrID: Evolution of Agent Controllers with a Hybrisation of Indirect and Direct Encodings
Neuroevolution, the optimisation of artificial neural networks (ANNs) through evolutionary computation, is a promising approach to the synthesis of controllers for autonomous agents. Traditional neuroevolution approaches employ direct encodings, which are limited in their ability to evolve complex or large-scale controllers because each ANN parameter is independently optimised. Indirect encodin...
متن کامل