Flocks, herds and schools: A distributed behavioral model
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Amalthaea: An Evolving Multi-Agent Information Filtering and Discovery System for the WWW
Autonomous Agents and Multi-Agent Systems
Grammatical Evolution: Evolving Programs for an Arbitrary Language
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Review: Intelligent Agents for Computer Games
CG '00 Revised Papers from the Second International Conference on Computers and Games
An Improved Genetic Algorithm with Average-bound Crossover and Wavelet Mutation Operations
Soft Computing - A Fusion of Foundations, Methodologies and Applications
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
Introduction to Genetic Algorithms
Introduction to Genetic Algorithms
Evolutionary design in a multi-agent design environment
Applied Soft Computing
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Implicit fitness functions for evolving a drawing robot
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Gradual distributed real-coded genetic algorithms
IEEE Transactions on Evolutionary Computation
Artificial worlds modeling of human resource management systems
IEEE Transactions on Evolutionary Computation
Constituent grammatical evolution
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
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This paper describes a novel approach to multi-agent simulation where agents evolve freely within their environment. We present Template Based Evolution (TBE), a genetic evolution algorithm that evolves behaviour for embodied situated agents whose fitness is tested implicitly through repeated trials in an environment. All agents that survive in the environment breed freely, creating new agents based on the average genome of two parents. This paper describes the design of the algorithm and applies it to a model where virtual migratory creatures are evolved to survive the simulated environment. Comparisons made between the evolutionary responses of the artificial creatures and observations of natural systems justify the strength of the methodology for species simulation.