Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Open Sources: Voices from the Open Source Revolution
Open Sources: Voices from the Open Source Revolution
A Non-Discrete Approach to the Evolution of Information Filtering Trees
BT Technology Journal
Coupling Developmental Rules and Evolution to Aid in Planning Network Growth
BT Technology Journal
Nature-Inspired Computing Technology and Applications
BT Technology Journal
The Advantages of Evolutionary Computation
Biocomputing and emergent computation: Proceedings of BCEC97
Core specification and experiments in DIET: a decentralised ecosystem-inspired mobile agent system
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
BT Technology Journal
A Non-Discrete Approach to the Evolution of Information Filtering Trees
BT Technology Journal
Hybrid Genetic Algorithms for Telecommunications Network Back-Up Routeing
BT Technology Journal
Coupling Developmental Rules and Evolution to Aid in Planning Network Growth
BT Technology Journal
Nature-Inspired Computing Technology and Applications
BT Technology Journal
Evolving Greenfield Passive Optical Networks
BT Technology Journal
DIET — A Scalable, Robust and Adaptable Multi-Agent Platform for Information Management
BT Technology Journal
Evolving preferences among emergent groups of agents
Adaptive agents and multi-agent systems
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The Eos platform supports research and rapid implementation of evolutionary algorithms, ecosystem simulations and hybrid models. It also supports fast prototyping of industrial applications using these technologies. A large and rapidly growing library of evolutionary algorithm types and options is provided which together with a flexible configuration system allows a 'plug-and-play' construction of novel algorithms. Support for ecosystem models includes classes for multiple types of physical space (n-dimensional discrete or continuous Cartesian space, graph space), complex interactions between entities, and movement of individuals between populations.The flexibility of the Eos platform is expected to provide a powerful environment for developing new algorithms and architectures. Eos is implemented in Java™ for portability and to allow easy extension of the core functionality. It supports transparent distribution of evolutionary and ecosystem implementations across multi-processor computer clusters. This paper describes the architecture and functionality of the Eos platform and illustrates its use by way of a number of example applications.